Original Article SARS-CoV-2-correlated ASGR1 is a novel potential marker for the treatment and identification of multiple human cancers
Tao Huang
Department of Cardiothoracic Vascular Surgery, The Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18 Zhongshan Second Road, Baise 533000, Guangxi Zhuang Autonomous Region, People’s Republic of China
Received June 14, 2022; Accepted November 25, 2022; Epub December 15, 2022; Published December 30, 2022
Abstract: Objectives: Cancer patients are reported to be more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the COVID-19 (the Corona Virus Disease 2019) patients with cancer suffer from certain serious complications. ASGR1 has been recently identified as a novel receptor of SARS-CoV-2 in human cells; however, there are limited studies on ASGR1 in various human cancers. Methods: This study utilized a com- prehensive analysis of COVID-19-related ASGR1 in multiple human cancers based on 18,589 multi-center samples. Using Wilcoxon rank-sum analysis, a difference in ASGR1 expression between cancer and control tissues was de- tected. Cox regression analysis, Kaplan-Meier curves, and receiver operating characteristic curves were utilized to determine the correlation between ASGR1 expression and the clinical parameters of cancer patients. The im- mune relevance and potential mechanisms of ASGR1 in various cancers were also investigated. Results: Abnormal ASGR1 mRNA expression was observed in 16 of 20 different cancers (e.g., it was upregulated in colon adenocarci- noma but downregulated in cholangiocarcinoma; P < 0.05). ASGR1 was related to prognosis, e.g., overall survival, in 14 cancers (P < 0.05), such as adrenocortical carcinoma. The gene was also found to be a potential marker that can be utilized to distinguish eleven cancers from controls with moderate to high accuracy (e.g., the area under the curve for cholangiocarcinoma = 1.000). ASGR1 expression was related to DNA methyltransferases, mismatch repair genes, immune checkpoints, levels of tumor mutational burden, microsatellite instability, neoantigen count, and immune infiltration levels in certain cancers (P < 0.05). The gene plays a role in multiple cancers by affecting four signaling pathways, such as cytokine-cytokine receptor interaction. Cancer patients with high ASGR1 expression are sensitive to 25 drugs, including ulixertinib. Conclusions: SARS-CoV-2-correlated ASGR1 is a novel marker that can be used for treating and identifying multiple human cancers.
Keywords: Prognosis, identification, immunology, tumor mutational burden, microsatellite instability, target thera- py
Introduction
COVID-19, the coronavirus disease 2019, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [1]. As of June 4, 2022, more than 53 million patients have been diagnosed worldwide, with more than six million deaths (https://coronavirus.jhu. edu/map.html). Despite years of clinical and scientific research on COVID-19, the numbers of infections and deaths remain unsatisfactory, and the emergence of mutant strains, such as Omicron, has exacerbated these problems [2]. Similar to COVID-19 pneumonia, cancer is also a disease that seriously affects human health.
During the COVID-19 pandemic, clinical and sci- entific researchers should focus on cancer patients because multiple serious events have been observed in cancer patients suffering from COVID-19 [3, 4]. More effort should be invested into cancer management, particularly during the COVID-19 pandemic.
Angiotensin converting enzyme 2 (ACE2) is known to be the host receptor of SARS-CoV-2; however, few reports focus on other host recep- tors of the virus, including asialoglycoprotein receptor 1 (ASGR1). This ASGR1, which is encoded by the gene ASGR1, is a subunit of ASGR protein. This ASGR protein participates in
ASGR1 in human cancers
serum glycoprotein homeostasis by affecting endocytosis and lysosome degradation [5]. As a subunit of ASGR protein, ASGR1 has the high- est expression level in oocytes, and its expres- sion in the embryonic all-energy eight-cell stage is dozens of times higher than that in embry- onic stem cells; these results indicate that ASGR1 may play an essential role in several critical stages of human development [5]. However, ASGR1 is also harmful to the human body in some cases. For instance, ASGR1 pro- tein is believed to facilitate infection with spe- cific viruses, such as hepatitis B [6] and SARS- CoV-2 [7]. During the COVID-19 pandemic, as an S protein-binding partner, ASGR1 was also identified as an alternative receptor of SARS- CoV-2 infection [7]. Moreover, ASGR1 may affect the target cell range of SARS-CoV-2 and antibody-mediated neutralization [7, 8]. Thus, ASGR1 is likely to play an essential role in infec- tion and the progression of COVID-19.
ASGR1 is also believed to be involved in the occurrence and progression of certain cancers. For example, ASGR1 is downregulated in liver hepatocellular carcinoma (LIHC) tissues as compared to non-tumorous tissues, and its low expression predicts an unfavorable prognosis for LIHC patients [9]. ASGR1 has also been determined to be negatively correlated with the tumor progression of LIHC in several studies [10, 11]. In addition to LIHC, ASGR1 also plays an organ-specific microenvironmental role in the growth and metastasis of colorectal carci- noma [12]. Therefore, ASGR1 is pivotal in these cancers. However, little is known about the gene in human cancers other than LIHC and colorectal carcinoma, and more research is required.
In this study, for the first time, the expression level, potential clinical value, and molecular mechanism of COVID-19-correlated ASGR1 in more than 30 kinds of human cancers are com- prehensively discussed via the analysis of thou- sands of samples, contributing to knowledge about the role of ASGR1 in multiple human cancers.
Materials and methods
Collection of ASGR1 mRNA information
The Genotype-Tissue Expression (GTEx) data- base [13] contains many specimens obtained
from homo sapiens, and it was used to investi- gate ASGR1 mRNA expression in normal human tissues in this study. The Cancer Genome Atlas (TCGA) includes RNA sequencing data from numerous cancer and control samples, which were utilized to explore the mRNA expression, clinical significance, and potential mechanisms of ASGR1 in a series of cancers. For the TCGA samples, three kinds of specimens were select- ed: (1) primary tumor tissue, (2) normal solid tissue, and (3) primary blood-derived cancer peripheral blood. Ultimately, a GTEx data set containing 8,671 specimens and a “TCGA- PANCAN” dataset including 9,773 TCGA sam- ples (9,054 cancer versus 719 control) were collected from the GTEx portal and Xena database (Supplementary Table 1). All mRNA expression values (transcripts per kilobase mil- lion) were downloaded from the two databases. The ASGR1 mRNA expression data were then processed by log2 (transcripts per kilobase mil- lion + 1) in R (Version 4.1.0).
Collection of ASGR1 protein information
The Human Protein Atlas [14] contains large quantities of cancer omics data. Using the “HPAanalyze” package [15] and the Atlas data- base, the Atlas’s immunohistochemical stain- ing data were screened for the initial investiga- tion of ASGR1 protein levels in cancer and con- trol tissues. The inclusion criteria for samples from the Human Protein Atlas were as follows: (1) not fewer than three cancer samples were found for each cancer type; (2) not fewer than three normal tissue specimens were collected for one cancer; and (3) for samples stained with several antibodies, only one type of antibody data was included. Consequently, 145 samples were included (three normal lung tissues were used for both LUAD and LUSC controls; Supplementary Table 1). The ASGR1 protein levels in tissues were reflected by the total immunohistochemical score, which was the product of a staining intensity score, as well as a quantity score (Supplementary Table 2).
Collection of clinical data and ASGR1 alterna- tions
The clinical features data were acquired from the Xena database, including the age, gender, and AJCC (American Joint Committee on Can- cer) stage of each patient. Four types of sur- vival data, including overall survival (OS), dis-
ease-specific survival (DSS), disease-free inter- val (DFI), and progression-free interval (PFI), were also collected from the same database. An overview of ASGR1 alternations in cancers was obtained from SangerBox (Version 3.0), and the utilized data were processed using MuTect2 software and derived from Genomic Data Commons Portal.
Extraction of expression data of DNA methyl- transferases, mismatch repair genes, and im- mune checkpoint genes
Three typical DNA methyltransferases (DNMT, i.e., DNMT1, DNMT3A, and DNMT3B), five mis- match repair genes (MMRs, i.e., MLH1, MSH2, MSH6, PMS2, and EPCAM), and 46 immune checkpoints (ICPs, BTLA, and other) were explored in this study. The expression data for DNMTs, MMRs, and ICPs were extracted from the TCGA dataset.
Collection of data on the tumor mutational burden, microsatellite instability, neoantigen count, and immune microenvironment
The tumor mutational burden (TMB), microsat- ellite instability (MSI), and neoantigen count data utilized in the current study were collected from a published paper [16]. Two scores, calcu- lated using the TIMER [17] and ESTIMATE algo- rithms, were used to explore the relevance of ASGR1 and the immune microenvironment. The TIMER score data and ESTIMATE scores were obtained from the TIMER website and SangerBox (Version 3.0).
Exploration of ASGR1’s underlying mecha- nisms in human cancers
For the 32 cancers investigated in this study, cancer patients were classified into high- and low-ASGR1 expression groups using the medi- an expression level for ASGR1. Gene set en- richment analysis (GSEA) was performed us- ing the “clusterProfiler” package [18], in which the differences in the Kyoto Encyclopedia of Genes and Genomes (KEGG) [19] signaling pathways between high- and low-ASGR1 ex- pression groups were explored. Via IC50 (half- maximal inhibitory concentration) values, Cell- Miner [20] can be used for testing the sensitiv- ity of drugs approved by the United States Food and Drug Administration or identified by clinical tests. Thus the tool was utilized to explore the sensitivity of ASGR1 to a series of drugs.
Statistical analysis
The Kruskal-Wallis test evaluated differential ASGR1 mRNA expression between distinct human tissues. The expression of ASGR1 between cancer and control tissues was com- pared via the Wilcoxon rank-sum test. The Wilcoxon rank-sum test was also employed to evaluate the relevance of ASGR1 expression to clinical features.
Whether ASGR1 expression was related to prognosis (i.e., OS, DSS, DFI, and PFI) was determined using univariate Cox regression analysis and Kaplan-Meier curves, as well as based on the “survival” and “forestplot” soft- ware packages. The identification value of ASGR1 expression for cancer status was evalu- ated through three parameters-the area under the curve (AUC), specificity, and sensitivity-of the receiver operating characteristic curves produced with the “pROC” software package [21]. All correlation analyses (i.e., ASGR1 with DNMTs, MMRs, ICPs, the immune environment, TMB, MSI, and neoantigen count) were carried out using Spearman’s rank correlation test. The Wilcoxon rank-sum test was utilized to select drugs potentially effective for patients with a high-ASGR1 expression. Statistical significance was indicated by p-values less than 0.05. Stata (Version 15.0) was applied to conduct the anal- ysis of the summary receiver operating charac- teristic curve, while R (Version 4.1.0) was used in the other analyses. Figure 1 demonstrates the overall design and results of the study.
Results
Differential expression of ASGR1 in various cancers and their control tissues
Distinct ASGR1 expression was detected in various normal human tissues. It was relatively upregulated in the liver, lung, and testis and downregulated in the kidney and muscle (P < 0.05; Figure 2A). In contrast to the control tis- sues, abnormal expression was observed in most cancers (16/20), with increasing expres- sion in COAD (colon adenocarcinoma), ESCA (esophageal carcinoma), HNSCC (head and neck squamous cell carcinoma), KIRC (kidney renal clear cell carcinoma), READ (rectum ade- nocarcinoma), and STAD (stomach adenocarci- noma) and decreasing expression in BRCA (breast invasive carcinoma), CHOL (cholangio- carcinoma), KICH (kidney chromophobe), LIHC
ASGR1 in human cancers
Difference in ASGR1 expression between normal tissues and cancer tissues
ASGR1 expression in: (1) normal tissues, (2) normal tissues and cancer tissues, and (3) cancer tissues at protein levels
ASGR1’s clinical relevance, prognosis significance, and identification value in cancers
Relationship between ASGR1 expression and (1) clinical features, (2) prognoses (overall survival, disease-specific survival, disease- free interval, and progression-free interval), and (3) cancer statuses
ASGR1’s relevance to DNMTs, MMRs, and the immune microenvironment
Relationship between ASGR1 expression and (1) DNMTs, (2) MMRs, and (3) the immune microenvironment
ASGR1’s correlation with ICPs, TMB, MSI, and neoantigen count
Correlation analyses of ASGR1 expression with (1) ICPs , (2) TMB levels, (3) MSI levels, and (4) neoantigen count
The underlying mechanisms of ASGR1 and drug susceptibility analysis
(1) Gene set enrichment analysis and (2) drugs that may be sensitive to ASGR1
SARS-COV-2-correlated ASGR1 is a novel marker for treatment and identification of multiple human cancers
(liver hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), PAAD (pancreatic adenocarcino- ma), PRAD (prostate adenocarcinoma), THCA (thyroid carcinoma), and UCEC (uterine corpus endometrial carcinoma) (P < 0.05; Figure 2B). No statistical difference in ASGR1 expression was found between normal tissues and BLCA (bladder urothelial carcinoma), GBM (glioblas- toma multiforme), KIRP (kidney renal papillary
cell carcinoma), and PCPG (pheochromocyto- ma and paraganglioma) tissues (P ≥ 0.05; Figure 2B).
While a trend of elevated ASGR1 protein levels was identified in COAD, kidney cancer, READ, consistent with the finding for mRNA levels (Figure 2C), all these differences were not sta- tistically significant, and thus more samples at protein levels would be needed to perform an
ASGR1 in human cancers
A
ASGR1 expression in normal tissues from various organs (sample number = 8671)
ASGR1 Expression Log2(TPM+1)
10.0-
Kruskal-Wallis, p < 2.2e-16
7.5
5.0
2.5
0.0
Adipose Tissue (554)
Adrenal Gland (152)
Bladder (11)
Blood (511)
Blood Vessel (707)
Brain (1272)
Breast (204)
Cervix Uteri (11)
Colon (357)
Esophagus (718)
Fallopian Tube (7)
Heart (433)
Kidney (30)
Liver (125)
Lung (313)
Muscle (417)
Nerve (310)
Ovary (100)
Pancreas (180)
Pituitary (119)
Prostate (111)
Salivary Gland (65)
Skin (883)
Small Intestine (102)
Spleen (112)
Stomach (191)
Testis (184)
Thyroid (321)
Uterus (84)
Vagina (87)
B
ASGR1 expression between cancers and their controls
ns
.
…
ns
.
ns
…
.
ns
.
ASGR1 mRNA Expression [log2(TPM+1)]
9
6
Group
Normal
Tumor
3
0
BLCA
BRCA
CHOL
COAD
ESCA
GBM
HNSCC
KICH
KIRC
KIRP
LIHC
LUAD
LUSC
PAAD
PCPG
PRAD
READ
STAD
THCA
UCEC
C
Breast (HPA011954)
COAD (HPA011954)
HNSCC (HPA012852
Kidney (HPA012852)
LIHC (HPA011954)
ASGR1 Protein Level
2
ASGR1 Protein Level
ASGR1 Protein Level
3
ASGR1 Protein Level
10
2
ASGR1 Protein Level
1
0.38
0.22
2
0.15
10
0.015
5
1
0.047
1
0
.
0
0
5
-1
0
—
-1
-1
0
-2
-5
-2
-2
Non-Tumor
Tumor
Non-Tumor
Tumor
Number (3 vs 11)
Non-Tumor
Tumor
Number (3 vs 3)
Non-Tumor
Tumor
Number (3 vs 12)
Non-Tumor
Tumor
Number (3 vs 6)
Number (3 vs 8)
LUAD (HPA012852)
LUSC (HPA012852)
PAAD (HPA012852)
PRAD (HPA011954)
READ (HPA011954)
ASGR1 Protein Level
2
ASGR1 Protein Level
2
ASGR1 Protein Level
9
ASGR1 Protein Level
ASGR1 Protein Level
9
6
0.54
3
1
1
2
0.87
6
0
0
3
1
3
2
-1
-1
0
0
0
2
-2
-1
-3
-3
Non-Tumor Tumor Number (3 vs 5)
Non-Tumor
Tumor
Number (3 vs 6)
Non-Tumor
Tumor
Number (3 vs 12)
Non-Tumor Number (3 vs 11) Tumor
Non-Tumor Number (3 vs 5) Tumor
STAD (HPA011954)
THCA (HPA011954)
UCEC (HPA011954)
ASGR1 Protein Level
20
ASGR1 Protein Level
2
ASGR1 Protein Level
2
0.54
10
1
1
0.035
0
0
0
-1
-1
-2
2
Non-Tumor
Tumor
Number (3 vs 11)
Non-Tumor
Tumor
Number (3 vs 4)
Non-Tumor
Tumor
Number (3 vs 12)
analysis of statistical significance. Under the microscope, ASGR1 protein levels remained consistent with their mRNA levels in certain cancers (Figure 3).
Relationship between ASGR1 expression and clinical features
For patients with ACC (adrenocortical carcino- ma), BLCA, HNSCC, KIRC, or TGCT (testicular
germ cell tumors), those in advanced AJCC stages were observed to have higher ASGR1 expression (P < 0.05; Figure 4A). For individu- als with LIHC, MESO (mesothelioma), PAAD, and THCA, a low stage of AJCC stages was related to upregulated ASGR1 expression (P < 0.05; Figure 4A). Male ACC patients and older (≥ 65 years old) LGG (brain lower grade glioma) patients had low ASGR1 expression, while BLCA, LAML (acute myeloid leukemia), and
ASGR1 in human cancers
Normal tissues
Cancer type
Cancer tissues
Normal tissues
Cancer type
Cancer tissues
☐
Breast
☐
COAD
☐
2773
2174
1423
Negative None
Weak <25%
Negative None
2948 Moderate >75%
HNSCC
☐
☐
Kidney
2547 Weak <25%
2547
Negative None
1767 Negative None
1969 Weak <25%
LIHC
LUAD
☐
24
25
Negativ None
2101 Negative None
1847 Negative None
Stro
>75%
LUSC
☐
☐
PAAD
2222 Negative None
2100 Negative None
3320 Negative None
823
Negative None
PRAD
☐
READ
☐
2053 Negative None
☐
3580
Moderate <25%
3231 Moderate <25%
3274 Moderate >75%
STAD
☐
☐
THCA
☐
2583 Strong 75% - 25%
2066 Moderate <25%
3005
Negative None
3267 Negative None
UCEC
☐
2242
3319
Negative None
Negative None
THCA patients aged at least 65 years had high ASGR1 expression (P < 0.05; Figure 4B and 4C). Except for this, no statistically sig- nificant difference in ASGR1 expression was found in patients with various clinical features (Supplementary Figures 1, 2 and 3).
Prognostic value of ASGR1
Using both univariate Cox analysis and Kaplan- Meier curves, ASGR1 expression was identified as an OS risk factor for patients with ACC, ESCA, KIRC, THCA, THYM (thymoma), and UVM (uveal melanoma; HR > 1, P < 0.05) and playing a protective role for LGG, LIHC, and PAAD patients (HR < 1, P < 0.05; Figure 5A and 5B). Most of the findings (except those for LIHC and THYM) were also determined in DSS (P <
0.05; Figure 5C and 5D). ASGR1 demonstra- ted poor PFI for individuals with ACC, HNSCC, KIRC, PCPG, PRAD, and TGCT, as well as unfa- vorable DFI for patients with ACC, COAD, and TGCT (HR > 1, P < 0.05; Figure 6A and 6B). It also demonstrated a favorable PFI for LGG and PAAD patients (HR < 1, P < 0.05; Figure 6A and 6C). All these results were also verified by Kaplan-Meier curves (P < 0.05; Figure 6C and 6D).
Identification significance of ASGR1 and the landscape of ASGR1 mutations
ASGR1 expression could conspicuously distin- guish cancer tissues from normal tissues in eleven of the 20 cancers (AUC > 0.7; Figure 7A). Notably, ASGR1 expression made it feasible to
ASGR1 in human cancers
A
ACC
BLCA
0.16
0.33
0.026
0.13
0.27
6
0.023
6
0.041
0.32
ASGR1 expression
0.2
0.29
0.84
ASGR1 expression
0.66
4
4
2
2
0
0
Stage | Stage II Stage III Stage IV AJCC_stage (n = 75)
Stage | Stage II Stage III Stage IV AJCC_stage (n = 405 )
LIHC
MESO
0.81
0,46
0.95
5
0.47
15
0.52
0.054
0.54
4
0.36
ASGR1 expression
0.011
ASGR1 expression
0.56
0.029
0.047
10
3
2
5
1
0
Stage | Stage II Stage III Stage IV AJCC_stage (n = 345 )
Stage | Stage II Stage III Stage IV AJCC_stage (n =87)
THCA
B
ACC
0.028
0.16
0.019
0.85
7.5
0.32
4
ASGR1 expression
0.077
0.36
ASGR1 expression
3
5.0
2
2.5
1
0.0
Stage | Stage II Stage III Stage IV AJCC_stage (n = 502 )
Female
Gender (n = 77 )
Male
C
BLCA
LAML
5
0.0079
4
0.039
ASGR1 expression
4
ASGR1 expression
3
3
2
2
1
1
0
<65
≥65
0
<65
Age_in_year (n = 407 )
Age_in_year (n = 173)
≥65
HNSCC
KIRC
0.77
8
0.046
0.09
0.74
0.15
0.046
9
0.001
0.45
0.0012
0.59
6
3
0
Stage | Stage II Stage III Stage IV AJCC_stage (n = 527 )
PAAD
1
0.098
0.16
6
0.038
0.081
ASGR1 expression
0.36
4
2
0
Stage | Stage II Stage III Stage IV AJCC_stage (n = 175 )
TGCT
0.67
0.031
6
0.11
ASGR1 expression
4
2
Stage I
Stage II AJCC_stage (n = 79 )
Stage III
differentiate CHOL, COAD, LUSC, READ, and THCA from their controls with a high level of accuracy (AUC = 0.852-1.000; Figure 7A). Over- all, the identification value of ASGR1 expres- sion in multiple cancers was determined by the values of specificity (0.87 [0.82-0.90]), sensi- tivity (0.64 [0.55-0.73]), and AUC (0.86 [0.83- 0.89]) (Figure 7B). ASGR1 mutations, including nonsense mutation, missense mutation, and frameshift insertion, were detected in certain cancers, particularly for SKCM, STAD, and UCEC (Figure 7C).
LGG
6
0.046
ASGR1 expression
4
2
Age_in_year (n = 508)
<65
≥65
THCA
6
0.031
ASGR1 expression
4
2
0
<65
Age_in_year (n = 504 )
≥65
ASGR1 with DNMTs, MMRs, TMB, MSI, and neoantigen count
ASGR1 expression was correlated with at least one of the three DNMTs (DNMT1, DNMT3A, and DNMT3B) in 29 of 32 cancer types. Moreover, ASGR1 expression was relevant to all three DNMTs in eleven cancers-BLCA, BRCA, GBM, HNSCC, KICH, KIRC, LIHC, OV (ovarian serous cystadenocarcinoma), TGCT, THCA, and UVM (P < 0.05; Figure 8A). ASGR1 expression was also significantly relevant, mainly negatively, to the
ASGR1 expression
6
0.56
4
ASGR1 expression
2
0
Stage | Stage II Stage III Stage IV AJCC_stage (n = 443 )
0.096
ASGR1 in human cancers
| A | Cancer (sample number) | Hazard ratio (95%CI) | ||
|---|---|---|---|---|
| ACC (n = 77) | < 0.05* | 1.691 | (1.258-2.275) | |
| BLCA (n = 398) | 0.200 | 0.881 | (0.730-1.063) | |
| BRCA (n = 1048) | 0.600 | 0.918 | (0.634-1.329) | |
| CHOL (n=33) | 0.800 | 1.025 (0.790-1.331) | ||
| COAD (n = 278) | 0.500 | 1.078 (0.862-1.349) | ||
| DLBC (n = 44) | 0.200 | 0.520 (0.174-1.553) | ||
| ESCA (n = 175) | < 0.05* | 1.225 | (1.023-1.466) | |
| GBM (n = 145) | 0.600 | 1.078 (0.806-1.440) | ||
| HNSCC (n = 510) | 0.100 | 1.290 | (0.985-1.690) | |
| KICH (n = 65) | 0.200 | 2.198 (0.671-7.198) | ||
| KIRC (n = 515) | < 0.05* | 1.459 (1.236-1.723) | ||
| KIRP (n = 276) | 0.900 | 1.033 (0.641-1.664) | ||
| LAML (n = 147) | 0.200 | 1.192 | (0.926-1.534) | |
| LGG (n = 474) | < 0.05* | 0.618 (0.487-0.784) | ||
| LIHC (n = 342) | < 0.05* | 0.899 (0.810-0.998) | ||
| LUAD (n = 490) | 0.300 | 0.889 (0.719-1.099) | ||
| LUSC (n = 471) | 0.100 | 1.188 | (0.948-1.489) | |
| MESO (n = 84) | 0.300 | 1.257 (0.829-1.904) | ||
| OV (n = 407) | 0.600 | 1.069 (0.809-1.412) | ||
| PAAD (n = 172) | < 0.05* | 0.500 (0.338-0.739) | ||
| PCPG (n = 170) | 0.300 | 1.733 (0.576-5.209) | ||
| PRAD (n = 492) | 0.700 | 1.298 (0.350-4.819) | ||
| READ (n = 91) | 1.000 | 1.006 (0.636-1.591) | ||
| SARC (n = 254) | 0.500 | 1.088 | (0.847-1.399) | |
| SKCM (n = 97) | 0.800 | 1.094 (0.544-2.201) | ||
| STAD (n = 374) | 0.400 | 1.067 | (0.921-1.235) | |
| TGCT (n = 128) | 0.700 | 0.836 | (0.284-2.462) | |
| THCA (n = 501) | < 0.05* | 2.003 (1.375-2.918) | ||
| THYM (n = 117) | < 0.05* | 2.428 (1.060-5.560) | ||
| UCEC (n = 166) | 0.600 | 1.115 | (0.729-1.704) | |
| UCS (n = 55) | 0.600 | 0.911 (0.617-1.344) | ||
| UVM (n = 74) | < 0.05* | 2.067 (1.037-4.121) | ||
C
Cancer (sample number)
p value
Hazard ratio (95%CI)
ACC (n = 75)
< 0.05*
1.767 (1.298-2.407)
BLCA (n = 385)
0.300
0.885 (0.707-1.108)
BRCA (n = 1029)
0.400
1.204 (0.764-1.896)
CHOL (n= 32)
0.900
1.017 (0.778-1.331)
COAD (n = 263)
0.100
1.280 (0.948-1.729)
DLBC (n = 44)
0.800
0.822 (0.207-3.272)
ESCA (n = 173)
< 0.05*
1.350 (1.101-1.655)
GBM (n = 132)
0.700
1.062 (0.775-1.455)
HNSCC (n = 486)
0.100
1.377 (0.990-1.914)
KICH (n = 65)
0.100
3.188 (0.878-11.569)
KIRC (n = 504)
< 0.05*
1.558 (1.294-1.876)
KIRP (n = 272)
0.600
1.163 (0.666-2.032)
LGG (n = 466)
< 0.05*
0.565 (0.440-0.726)
LIHC (n = 334)
0.500
0.950 (0.819-1.102)
LUAD (n = 457)
0.200
0.835 (0.640-1.090)
LUSC (n = 421)
0.100
1.366 (0.974-1.914)
MESO (n = 64)
0.200
1.419 (0.852-2.362)
OV (n = 378)
0.800
1.038 (0.767-1.405)
PAAD (n = 166)
< 0.05*
0.573 (0.377-0.869)
PCPG (n = 170)
0.100
3.017 (0.895-10.172)
READ (n = 85)
0.800
0.872 (0.368-2.064)
SARC (n = 248)
0.500
1.101 (0.834-1.453)
SKCM (n = 97)
0.500
1.302 (0.600-2.827)
STAD (n = 353)
0.100
1.150 (0.968-1.366)
TGCT (n = 128)
0.900
0.885 (0.244-3.218)
THCA (n = 495)
< 0.05*
2.553 (1.492-4.370)
THYM (n = 117)
0.400
1.851 (0.445-7.706)
UCEC (n = 164)
0.800
0.918 (0.514-1.638)
UCS (n = 53)
0.700
0.916 (0.611-1.375)
UVM (n = 74)
< 0.05*
2.121 (1.020-4.413)
B
ACC
ESCA
KIRC
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
0.75
0.75
0.75
0.50
0.50
0.50
0.25
p < 0.0001
0.25
p
=
0.25
p < 0.0001
0.00
0.00-
0.00
0
2.5
5
7.5
10
12.5
0
2.5
5
7.5
10
0
2.5
5
7.5
10
12.5
Time (Years)
Time (Years)
Time (Years)
LGG
LIHC
PAAD
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
0.75
0.75
0.75
0.50
0.50
0.50
0.25
p < 0.000
0.25
p = 0.001
0.25
p
0.00
0.00
0.00
0
5
10
15
20
0
2.5
5
7.5
10
0
2
4
6
8
Time (Years)
Time (Years)
Time (Years)
THCA
THYM
UVM
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
0.75
0.75
0.75
.0.50
0.50
0.50
0.25
p = 0.00028
0.25
p
=
0.00057
0.25
p < 0.000
1
0.00
0.00
0.00
0
5
10
15
0
2.5
5
7.5
10
Time (Years)
12.5
0
2
4
6
Time (Years)
8
Time (Years)
0.250.50 1.0 2.0 4.0 Overall survival
D
ACC
ESCA
KIRC
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
0.75
0.75
0.75
0.50
0.50
0.50
0.25
p < 0.0001
0.25
p
0.0001
0.25
p < 0.0001
0.00
0.00
0.00
0
2.5
5
7.5
10
12.5
0
2.5
5
7.5
10
0
2.5
5
7.5
10
12.5
Time (Years)
Time (Years)
Time (Years)
LGG
PAAD
THCA
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
0.75
0.75
0.75
0.50
0.50
0.50
0.25
p
< 0.0001
0.25
p = 0.0019
0.25
p < 0.0001
0.00
0.00
0.00
0
5
10
15
20
0
2
4
6
Time (Years)
8
0
5
10
Time (Years)
Time (Years)
15
UVM
Survival probability
1.00
0.75
ASGR1 Expression
0.50
High
0.25
p < 0.0001
Low
0.00
0
2
4
6
Time (Years)
8
0.062 0.250 1.00 4.00 Disease specific survival
five MMRs-MLH1, MSH2, MSH6, PMS2, and EPCAM-in BRCA and LIHC (P < 0.05; Figure 8B).
Because TMB and MSI have been considered feasible markers of immunotherapy response in tumor patients [22-24], this investigation explored their relevance to ASGR1 expression.
The findings showed that TMB was significantly associated with ASGR1 expression in ACC (p = 0.440; P < 0.05), COAD, LAML, and GBM (p < -0.2; P < 0.05) (Figure 8C). ASGR1 expression was also relevant to MSI in certain cancers, such as TGCT and STAD (P < 0.05; Figure 8D). Neoantigens have the potential to be tumor
PRAD (n = 490)
0.600
0.527 (0.056-4.918)
ASGR1 in human cancers
0.50
1.0
2.0
4.0
| A | Cancer (sample number) p value | Hazard ratio (95%CI) | |
| ACC (n = 76) | < 0.05* | 1.877 (1.440-2.445) | |
| BLCA (n = 397) | 0.600 | 0.957 (0.797-1.150) | |
| BRCA (n = 1047) | 0.100 | 1.347 (0.968-1.875) | |
| CHOL (n=33) | 0.600 | 1.063 (0.863-1.311) | |
| COAD (n = 275) | 0.200 | 1.147 (0.939-1.400) | |
| DLBC (n = 43) | 0.900 | 1.083 (0.446-2.626) | |
| ESCA (n = 173) | 0.200 | 1.125 (0.923-1.372) | |
| GBM (n = 144) | 0.800 | 0.961 (0.724-1.275) | |
| HNSCC (n = 509) | < 0.05* | 1.398 (1.084-1.802) | |
| KICH (n = 65) | 0.100 | 2.372 (0.991-5.676) | |
| KIRC (n = 508) | < 0.05* | 1.327 (1.096-1.607) | |
| KIRP (n = 273) | 0.900 | 1.027 (0.667-1.583) | |
| LGG (n = 472) | < 0.05* | 0.606 (0.501-0.732) | |
| LIHC (n = 341) | 0.300 | 0.953 (0.861-1.053) | |
| LUAD (n = 486) | 0.400 | 0.918 (0.755-1.117) | |
| LUSC (n = 471) | 0.200 | 1.194 (0.914-1.559) | |
| MESO (n = 82) | 0.300 | 1.257 (0.798-1.978) | |
| OV (n = 407) | 0.400 | 1.111 (0.860-1.435) | |
| PAAD (n = 171) | < 0.05* | 0.551 (0.381-0.796) | |
| PCPG (n = 168) | < 0.05* | 3.277 (1.704-6.303) | |
| PRAD (n = 492) | < 0.05* | 1.732 (1.254-2.392) | |
| READ (n = 90) | 0.800 | 1.050 (0.690-1.598) | |
| SARC (n = 250) | 1.000 | 1.002 (0.810-1.240) | |
| SKCM (n = 96) | 0.600 | 0.826 (0.421-1.621) | |
| STAD (n = 377) | 0.100 | 1.132 (0.972-1.318) | |
| TGCT (n = 126) | < 0.05* | 1.345 (1.030-1.757) | |
| THCA (n = 500) | 1.000 | 1.003 (0.724-1.390) | |
| THYM (n = 117) | 0.300 | 1.382 (0.704-2.714) | |
| UCEC (n = 166) | 0.200 | 0.732 (0.462-1.159) | |
| UCS (n = 55) | 0.600 | 0.901 (0.620-1.308) | |
| UVM (n = 73) | 0.100 | 1.835 (0.962-3.502) | |
Progression free interval
| B | Cancer (sample number) | p value | Hazard ratio (95%CI) | |||
|---|---|---|---|---|---|---|
| ACC (n = 44) | < 0.05* | 2.122 (1.282-3.513) | ||||
| BLCA (n = 184) | 0.600 | 1.129 (0.737-1.728) | ||||
| BRCA (n = 908) | 0.200 | 1.354 (0.874-2.099) | ||||
| CHOL (n=23) | 1.000 | 1.008 (0.771-1.319) | ||||
| COAD (n = 103) | < 0.05* | 1.962 (1.291-2.982) | ||||
| DLBC (n = 26) | 0.600 | 1.530 (0.309-7.584) | ||||
| ESCA (n = 84) | 0.900 | 0.962 (0.504-1.838) | ||||
| HNSCC (n = 128) | 0.100 | 1.492 (0.931-2.391) | ||||
| KICH (n = 29) | 0.100 | 8.434 (0.794-89.646) | ||||
| KIRC (n = 113) | 0.300 | 0.574 (0.192-1.716) | ||||
| KIRP (n = 177) | 0.200 | 1.398 (0.859-2.276) | ||||
| LGG (n = 126) | 0.300 | 0.729 (0.407-1.305) | ||||
| LIHC (n = 295) | 0.300 | 0.948 (0.850-1.058) | ||||
| LUAD (n = 295) | 0.400 | 0.876 (0.651-1.180) | ||||
| LUSC (n = 292) | 0.600 | 1.105 (0.725-1.686) | ||||
| MESO (n = 14) | 0.100 | 3.164 (0.779-12.852) | ||||
| OV (n = 203) | 0.700 | 0.929 (0.638-1.354) | ||||
| PAAD (n = 68) | 0.100 | 0.515 (0.222-1.198) | ||||
| PCPG (n = 152) | 0.100 | 0.093 (0.006-1.382) | ||||
| PRAD (n = 337) | 0.600 | 1.223 (0.558-2.678) | ||||
| READ (n = 29) | 0.600 | 0.802 (0.339-1.900) | ||||
| SARC (n = 149) | 0.800 | 0.969 (0.711-1.320) | ||||
| STAD (n = 232) | 0.100 | 1.218 (0.940-1.578) | ||||
| TGCT (n = 101) | < 0.05* | 1.396 (1.020-1.911) | ||||
| THCA (n = 352) | 0.500 | 0.848 (0.508-1.416) | ||||
| UCEC (n = 115) | 0.200 | 0.637 (0.307-1.320) | ||||
| UCS (n = 26) | 0.200 | 0.584 (0.276-1.236) | ||||
0.008 0.125 2.000 32.000 Disease free interval
C
ACC
HNSCC
KIRC
LGG
ACC
COAD
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
0.75
0.75
0.75
0.75
0.75
0.75
0.50
0.50
0.50
0.50
0.50
0.50
0.25
p
0.25
p
<
0.0001 1
0.25
p < 0.0001
0.25
p
0001
0.25
P
=
0.0025
0.25
p = 0.00023
0.00
0.00
0.00
0.00
0.00
0.00
0
2.5
5
7.5
10
12.5
0
5
10
15
20
0
3
6
9
12
0
5
10
15
0
2.5
5
7.5
10
12.5
0
3
6
9
Time (Years)
Time (Years)
Time (Years)
Time (Years)
Time (Years)
Time (Years)
12
PAAD
PCPG
PRAD
TGCT
TGCT
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
Survival probability
1.00
0.75
0.75
0.75
0.75
0.75
ASGR1 Expression
0.50
0,50
0.50
.0.50
0.50
High
0.25
p
0.000
0.25
p = 0.00025
0.25
p < 0.0001
0.25
p
= 0.0014
0.25
p
=
0.0011
1
Low
0.00
0.00
0.00
0.00
0.00
0
2
4
6
8
0
5
10
15
20
0
5
10
Time (Years)
15
0
5
10
15
0
Time (Years)
20
5
10
15
Time (Years)
20
Time (Years)
Time (Years)
antigens and thus affect the immune response [22-24]. In this study, Spearman rank correla- tion analysis revealed a relationship between ASGR1 expression and the neoantigen count in CHOL and PCPG (P < 0.05; Figure 8E), suggest- ing that ASGR1 may participate in the process- es of immune response to cancers.
ASGR1 with an immune microenvironment and ICPs
The correlation of ASGR1 expression with six kinds of infiltrating immune cells (B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells) was explored in this study. ASGR1 expression showed significantly nega-
tive relationship with the infiltration levels of all six immune cells in CHOL (p ≤-0.39, P < 0.05), while it was positively associated with the six immune cells in KIRP and HNSCC (p = 0.29- 0.58, P < 0.05) (Figure 9A). The close associa- tion between ASGR1 expression and the immune microenvironment in CHOL, KIRP, and HNSCC was confirmed via ESTIMATE scores (p = - 0.52 to 0.59, P < 0.05; Figure 9B). In DLBC, LAML, and KICH, ASGR1 expression demon- strated a strong correlation with the immune microenvironment (p ≥ 0.48, P < 0.05; Figure 9B). There were also statistical links between ASGR1 expression and the immune microenvi- ronment in specific cancers (Supplementary Figures 4, 5).
ASGR1 in human cancers
A
BLCA (n = 19 vs 407)
BRCA (n = 113 vs 1092)
CHOL (n= 9 vs 36)
COAD (n = 41 vs 288)
ESCA (n = 13 vs 181)
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
0.4
AUC: 0.596
0.4
AUC: 0.571
0.4
AUC: 1.000
0.4
AUC: 0.854
0.4
AUC: 0.774
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
GBM (n = 5 vs 153)
HNSCC (n = 44 vs 518)
KICH (n = 25 vs 66)
KIRC (n = 72 vs 530)
KIRP (n = 32 vs 288)
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
0.4
AUC: 0.635
0.4
AUC: 0.605
0.4
AUC: 0.736
0.4
AUC: 0.843
0.4
AUC: 0.579
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
LIHC (n = 50 vs 369)
LUAD (n = 59 vs 513)
LUSC (n = 50 vs 498)
PAAD (n = 4 vs 178)
PCPG (n = 3 vs 177)
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
0.4
AUC: 0.654
0.4
AUC: 0.679
0.4
AUC: 0.886
0.4
AUC: 0.794
0.4
AUC: 0.819
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
PRAD (n = 52 vs 495)
READ (n = 10 vs 92)
STAD (n = 36 vs 414)
THCA (n = 59 vs 504)
UCEC (n = 23 vs 180)
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
Sensitivity
0.8
0.4
AUC: 0.584
0.4
AUC: 0.876
0.4
AUC: 0.718
0.4
AUC: 0.852
0.4
AUC: 0.693
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
0.0
0.4
0.8
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
1 - Specificity
B
1.0
C
LGG(N=500,0.2%)-
LUAD(N=508,0.2%)-
Missense_Mutation
ESCA(N=180,0.6%)-
Nonsense_Mutation
KIRP(N=279,0.4%)-
Frame_Shift_Ins
STAD(N=409,1.5%)-
Sensitivity
PRAD(N=492,0.4%)
UCEC(N=175,1.7%)-
0.5
HNSCC(N=498,0.2%)-
LUSC(N=485,0.2%)-
Observed Data
LIHC(N=356,0.3%)-
Summary Operating Point
OV(N=303,0.3%)-
SENS = 0.64 [0.55 0.73]
SPEC = 0.87 |0.82 - 0.90]
SKCM(N=102,1.0%)-
SROC Curve
BLCA(N=407,0.7%)
AUC = 0.86 [0.83 - 0.89]
- 95% Confidence Contour
95% Prediction Contour
0.0
1.0
0.5
0.0
291aa
Lectin_N
CLECT_DC-SIGN_like
Specificity
ICP disorder has been proven to regulate immune cells [25]. The correlation between ASGR1 and the immune microenvironment may be attributed to the expression link between ASGR1 and ICPs because the gene was signifi- cantly related to a series of ICPs (Supplementary Figure 6). For example, in BRCA and TGCT, ASGR1 expression was associated with at least
20 ICPs expression (p ≥ 0.3 or p ≤-0.3, P < 0.05; Supplementary Figure 6).
GSEA and sensitive drug analysis
In this study, GSEA was performed to explore the underlying mechanisms of ASGR1 in 32 cancers. Four KEGG signaling pathways-olfac-
ASGR1 in human cancers
A
Correlation between ASGR1 expression and DNA methyltransferase expression
DNMT3B
S
..
p value
1
DNMT3A
0
DNMT1
…
=
ACC (n = 77)
BLCA (n = 407)
BRCA (n = 1092)
CHOL (n=36)
COAD (n = 288)
DLBC (n = 47)
ESCA (n = 181)
GBM (n = 153)
HNSCC (n = 518)
KICH (n = 66)
KIRC (n = 530)
KIRP (n = 288)
LAML (n = 173)
LGG (n = 509)
LIHC (n = 369)
LUAD (n = 513)
LUSC (n = 498)
MESO (n = 87)
OV (n = 419)
PAAD (n = 178)
PCPG (n = 177)
PRAD (n = 495)
READ (n = 92)
SARC (n = 258)
SKCM (n = 102)
STAD (n = 414)
TGCT (n = 148)
THCA (n = 504)
THYM (n = 119)
UCEC (n = 180)
UCS (n = 57)
UVM (n = 79)
Spearman p
0.6
-0.3
B
Correlation between ASGR1 expression and mismatch repair genes expression
EPCAM
p value
0.9
PMS2
MSH6
MSH2
MLH1
ACC (n = 77)
BLCA (n = 407)
BRCA (n = 1092)
CHOL (n=36)
COAD (n = 288)
DLBC (n = 47)
ESCA (n = 181)
GBM (n = 153)
HNSCC (n = 518)
KICH (n = 66)
KIRC (n = 530)
KIRP (n = 288)
LAML (n = 173)
LGG (n = 509)
LIHC (n = 369)
LUAD (n = 513)
LUSC (n = 498)
MESO (n = 87)
OV (n = 419)
PAAD (n = 178)
PCPG (n = 177)
PRAD (n = 495)
READ (n = 92)
SARC (n = 258)
SKCM (n = 102)
STAD (n = 414)
TGCT (n = 148)
THCA (n = 504)
THYM (n = 119)
UCEC (n = 180)
UCS (n = 57)
UVM (n = 79)
0 Cor
C
COAD ***
LAML* GBM ** ACC ***
D
COAD.STAD ** “TGCT*
THYM*
ACC
BRCA ***
PAAD
0.5
BLCA ***
UCS
0.2
BLCA*
STAD*
0.25
KIRC **
KICH
0.1
ESCA
SKCM
TGCT
THYM
SKCM
0
0
MESO
OV*
THCA
READ
U
-0.1
LUAD
-0.5
BRCA ***
PCPG
0.
MESO
UVM
KIRP
PAAD
LGG
KICH
UCEC
SARC
LUSC
READ
PRAD
LIHC
LAML
ESCA
UCS
CHOL
UVM
LGG
LIHC
KIRC
GBM
CHOL
THCA
UCEC
LUAD
DLBC
SARCHNSCCPCPG
LUSC
DLBC
OV
KIRP
HNSCC PRAD
E
CHOL (n=30)
PCPG (n = 60)
8
ASGR1 Log2(TPM+1)
8-
1p =- 0.21, p = 0.25
ASGR1 Log2(TPM+1)
4
p = 0.26, p = 0.043
6
3
4
2
2
1
0
2
4
6
0
1
2
3
4
Log2(neoantigen count + 1)
Log2(neoantigen count + 1)
tory transduction, cytokine-cytokine receptor interaction, chemokine signaling pathway, and neuroactive ligand receptor interaction-were found for at least 25% (8/32) of cancers (Supplementary Table 3), suggesting that ASGR1 may influence these cancers by affect-
ing the four signaling pathways. ASGR1 was observed to play complex roles in seven can- cers (ACC, BRCA, BLCA, CHOL, KICH, KIRP, and LAML), because at least five KEGG signaling pathways were found in these cancers (P < 0.05; Figure 10).
ASGR1 in human cancers
A
CHOL (n=36)
CHOL (n=36)
CHOL (n= 36)
CHOL (n=36)
CHOL (n=36)
CHOL (n=36)
ASGR1 Log2(TPM+1)
0.45, p = 0.007
ASGR1 Log2(TPM+1)
0.39, p = 0.019
ASGR1 Log2(TPM+1)
8p =- 0.49, p = 0.003
ASGR1 Log2(TPM+1)
8
p =- 0.4, p = 0.016
ASGR1 Log2(TPM+1)
8
0.46, p = 0.0049
ASGR1 Log2(TPM+1)
8
.52, p = 0.0013
6
6
6
6-
6
4
3
3
A
4-
+
2
0
0
N
2
-
N
3
3
0
0
0
0.15
0.20
0.25
0.30
0.35
0.14
0.16
6 0.18 0.20 0.22
CD4_Tcell level
0.17
0.18
B_cell level
0.19
CD8_Tcell level
0.20
0.075
0.080
0.085
0.090
0.54
Neutrophil level
0.035 0.040 0.045 0.050 Macrophage level
0.56
Dendritic level
0.58
KIRP (n = 288)
KIRP (n = 288)
KIRP (n = 288)
KIRP (n = 288)
KIRP (n = 288)
KIRP (n = 288)
ASGR1 Log2(TPM+1)
.8
p = 0.4, p = 1.88-12
ASGR1 Log2(TPM+1)
8
p = 0.41, p = 3.4e-13
ASGR1 Log2(TPM+1)
8
p = 0.37, p = 1.4e-10
ASGR1 Log2(TPM+1)
8
p = 0.43, p = 2.5e-14
ASGR1 Log2(TPM+1)
.8
p = 0.39, p = 3.88-12
ASGR1 Log2(TPM+1)
8
p = 0.51, p < 2.2e-16
6
6-
6
6-
6
6
4
4
A
4
4%
4.
2
2
2
:
.
₦
2
&
0
0
0
0
0
0
0.0
0.2
0.4
0.6
0.0
0.1
0.2
0.3
0.4
0.0
0.5
0.0
0.4
0.4
B_cell level
CD4_Tcell level
CD8_Tcell level
1.0
1.5
2.0
0.1
0.2
0.2
0.6
0.8
0.8
1.2
Neutrophil level
Macrophage level
Dendritic level
1.6
HNSCC (n= 512)
HNSCC (n = 512)
HNSCC (n = 512)
HNSCC (n = 512)
HNSCC (n = 512)
HNSCC (n = 512)
ASGR1 Log2(TPM+1)
8
p = 0.29, p = 3.88-11
ASGR1 Log2(TPM+1)
8
p = 0.37, p < 2.20-16
ASGR1 Log2(TPM+1)
8
p = 0.35, p < 2.2e-16
ASGR1 Log2(TPM+1)
8
p = 0.31, p = 5.5e-13
ASGR1 Log2(TPM+1)
8
p = 0.58, p < 2.20-16
ASGR1 Log2(TPM+1)
8
p = 0.45, p < 2.2e-16
6
6
6
6-
6
6
4
4
A
4
4
2
2
₦
N
2
2
0-
0
1
0
M
2
0
0.0
0.5
1.0
1.5
0.0
0.3
0.6
0.9
1.2
0.0
0.5
1.0
1.5
1
CD4_Tcell level
CD8_Tcell level
0.0
Neutrophil level
0.2
0.4
0.0
0.2
0.4
0.6
0.5
1.0
B_cell level
Macrophage level
Dendritic level
1.5
B
CHOL (n= 36)
KIRP (n = 285)
HNSCC (n = 517)
DLBC (n = 46)
LAML (n = 149)
KICH (n = 65)
ASGR1 Log2(TPM+1)
8
p = +0.42, p = 0.011
ASGR1 Log2(TPM+1)
6
p = 0.46, p = 4.5e-16
ASGR1 Log2(TPM+1)
5
p = 0.46, p . 2.20-16
ASGR1 Log2(TPM+1)
3-
p = 0.71, p = 3.60-08
ASGR1 Log2(TPM+1)
p = 0.58, p =. 1.7e-14
ASGR1 Log2(TPM+1)
p = 0.6, p = 1.4e-07
4
3
6-
4-
2
3
2
N
N
1
N
I
2
1
0
0
·
.
O
-2000 1500-1000-500 0
Stromal_score
500
-2000
-1000
Stromal_score
0
1000
-2000 -1000
0
Stromal_score
1000
-500
0
500
0
-2000
0
Stromal_score
-1500 -1000 -500
Stromal_score
0
-1000
Stromal_score
CHOL (n=36)
KIRP (n = 285)
HNSCC (n = 517)
DLBC (n = 46)
LAML (n = 149)
KICH (n = 65)
ASGR1 Log2(TPM+1)
8-
P= - 0.52, p = 0.0014
ASGR1 Log2(TPM+1)
p = 0.59, p < 2.2e-16
ASGR1 Log2(TPM+1)
5
p = 0.39, p < 2.2e-16
ASGR1 Log2(TPM+1)
-3-
p = 0.55, p= 0.000067
ASGR1 Log2(TPM+1)
p = 0.64, p < 2.2e.16
ASGR1 Log2(TPM+1)
p = 0.48, p = 0.000048
6
4
3
4
2
.
4
%
2
N
2
2-
1
1
1
0
C
O
0
·
-1000
0
1000 2000 3000
-1000
100020003000
0
0
-1000
0
1000 2000 3000
2800
3200
3600
0
1000
Immune_score
Immune_score
Immune_score
2400
Immune_score
150020002500300035004000
Immune_score
-1000
Immune_score
CHOL (n= 36)
KIRP (n = 285)
HNSCC (n = 517)
DLBC (n = 46)
LAML (n = 149)
KICH (n = 65)
ASGR1 Log2(TPM+1)
8-
O
-0.49, p = 0.0025
ASGR1 Log2(TPM+1)
6
p = 0.57, p < 2.2e-16
ASGR1 Log2(TPM+1)
5
p = 0.49,.p < 2.2e-16
ASGR1 Log2(TPM+1)
3-
p = 0.77, p = 5.3e-10.
ASGR1 Log2(TPM+1)
p = 0.65, p < 2:20-16
ASGR1 Log2(TPM+1)
p = 0.56, p = 1.3e-06
5
3
4
2
#
3
2.
N
2
N
2
I
·
0
0
0
0
O
-2000
0
2000
ESTIMATE_score
4000 -2000
0
2000 4000
0
ESTIMATE_score
-2000
2000 4000
a
ESTIMATE_score
2000 2500 3000 3500 4000
ESTIMATE_score
0
1000 2000 3000 4000
ESTIMATE_score
-300020001000 0
10002000
ESTIMATE_score
Because ASGR1 is likely a marker that can be used for treating multiple cancers, this study explored drugs that were potentially sensitive to ASGR1. Up to 25 of these 57 drug types were sensitive to ASGR1 because as lower IC50 val- ues were observed in the high ASGR1 expres- sion group (P < 0.05; Supplementary Figure 7).
Discussion
COVID-19 has attracted increasing attention and posed a global public health threat since
December 2019 [1]. Cancer patients have been found to be particularly susceptible to SARS- CoV-2, and COVID-19 patients with cancer often have severe complications [3, 26]. Thus, the COVID-19 epidemic has created a great chal- lenge in terms of managing cancer patients. ASGR1 has been recently identified as a novel receptor of SARS-CoV-2 in human cells and plays an essential role in several human can- cers [7]. Nevertheless, to the best of my knowl- edge, no research to date has examined ASGR1 in a variety of human cancers.
ASGR1 in human cancers
ACC
KEGG_AUTOIMMUNE_THYROID_DISEASE
BRCA
KEGG_CHEMOKINE_SIGNALING_PATHWAY
1.01
Running Enrichment Score
KEGG_COMPLEMENT_AND_COAGULATION_CASCADES
Running Enrichment Score
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
0.5 -
KEGG_DRUG_METABOLISM_OTHER_ENZYMES
0.5
KEGG_JAK_STAT_SIGNALING_PATHWAY
KEGG_OLFACTORY_TRANSDUCTION
KEGG_PROXIMAL_TUBULE_BICARBONATE_RECLAMATION
0.0
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS
0.0
KEGG_RETINOL_METABOLISM
-0.5
-0.5
=1.0
-1.0-
IL
I
Ranked List Metric
20
Ranked List Metric
10
10
0
0
-10
-10
-20
10000
20000
30000
40000
50000
10000
20000
30000
40000
50000
Rank in Ordered Dataset
Rank in Ordered Dataset
BLCA
KEGG_CELL_ADHESION_MOLECULES_CAMS
CHOL
KEGG_CHEMOKINE_SIGNALING_PATHWAY
Running Enrichment Score
KEGG_CHEMOKINE_SIGNALING_PATHWAY
0.00
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
0.75
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
Running Enrichment Score
KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY
KEGG_DILATED_CARDIOMYOPATHY
-0.25
KEGG_OLFACTORY_TRANSDUCTION
0,50
KEGG_HEMATOPOIETIC_CELL_LINEAGE
KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY
-0.50
0.25
-0.75
0,00
I
Ranked List Metric
Ranked List Metric
20
10
10-
0
0
-10
-10-
10000
20000
30000
40000
50000
-20
10000
20000
30000
40000
50000
Rank in Ordered Dataset
Rank in Ordered Dataset
KICH
KEGG_CHEMOKINE_SIGNALING_PATHWAY
KIRP
KEGG_CELL_ADHESION_MOLECULES_CAMS
1.00-
1.00
Running Enrichment Score
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
Running Enrichment Score
KEGG_CHEMOKINE_SIGNALING_PATHWAY
0.75
KEGG_HEMATOPOIETIC_CELL_LINEAGE
0.75
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
KEGG_PRIMARY_IMMUNODEFICIENCY
KEGG_HEMATOPOIETIC_CELL_LINEAGE
0.50
KEGG_STARCH_AND_SUCROSE_METABOLISM
0.50
KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION
0.25
0.25
0.00
0.00-
Ranked List Metric
20
Ranked List Metric
10
10
0
0
-10
-10
10000
20000
30000
40000
50000
10000
20000
30000
40000
50000
Rank in Ordered Dataset
Rank in Ordered Dataset
LAML
KEGG_CELL_ADHESION_MOLECULES_CAMS
Running Enrichment Score
KEGG_ECM_RECEPTOR_INTERACTION
0.75
KEGG_MAPK_SIGNALING_PATHWAY
KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION
0.50-
KEGG_OLFACTORY_TRANSDUCTION
0.25
0.00
Figure 10. Gene set enrichment analysis of ASGR1 in pan-cancer.
Ranked List Metric
20
10
0
-10
10000
20000
30000
40000
50000
Rank in Ordered Dataset
ASGR1 in human cancers
To fill this gap, this study provides a compre- hensive analysis of COVID-19-related ASGR1 in multiple human cancers. Differential ASGR1 expression at the mRNA and protein levels among normal and cancer tissues was dis- cussed based on 16,514 samples. ASGR1 was related to several types of prognoses (OS, DSS, DFI, and PFI) for certain cancers. It was also determined, for the first time, to be a potential marker used in distinguishing cancers from their controls with moderate to high accuracy. The underlying mechanism of ASGR1 in multi- ple cancers was also explored through a corre- lation investigation with TMB, MSI, neoantigen, ICPs, and so forth.
Abnormal expression was detected among various human organs and most cancers. Upregulated ASGR1 expression was investigat- ed in the liver, lung, and testis, and the results for the testis have been confirmed previously before [6]. The results for mRNA levels in LIHC in this study were consistent with those of Shi et al. [11], who demonstrated downregulated ASGR1 protein levels in LIHC. In addition to LIHC, this study also revealed elevated ASGR1 mRNA expression in COAD, ESCA, HNSCC, KIRC, READ, and STAD and decreased ASGR1 mRNA expression in BRCA, CHOL, KICH, LIHC, LUAD, LUSC, PAAD, PRAD, THCA, and UCEC as compared to the control tissues. The explora- tion of ASGR1 protein levels also confirmed most of these results.
The differential expression of ASGR1 demon- strated a correlation with cancer prognosis and status in specific cancers. This study iden- tified lower ASGR1 expression in LIHC patients with later clinical stages, as confirmed by previ- ous study [9]. It also revealed a correlation between ASGR1 expression and a series of clinical features in specific cancers. For exam- ple, for patients with ACC, BLCA, HNSCC, KIRC, or TGCT, higher AJCC stages were observed to be associated with elevated ASGR1 expres- sion. In terms of the prognostic value of ASGR1, Zhang et al. [10] reported that ASGR1 expres- sion is negatively related to LIHC progression and OS in LIHC patients. This finding was veri- fied in this study. Moreover, this study also found ASGR1 expression to be negatively asso- ciated with prognosis in patients with ACC, COAD, ESCA, HNSCC, KIRC, PCPG, PRAD, TGCT, THCA, THYM, and UVM and positively related with the prognosis in individuals with LGG and
PAAD in terms of OS, DSS, DFI, or PFI. To the best of my knowledge, these results have not been previously reported. Moreover, this study determines, for the first time, the essential value of ASGR1 expression in identifying can- cer status, particularly for CHOL, COAD, LUSC, READ, and THCA. Therefore, ASGR1 may serve as a novel marker for predicting the prognosis and status of multiple cancers.
ASGR1 may affect some aspects of genomic heterogeneity. DNMTs are known to affect gene expression without changing DNA sequences [27, 28], and the intense expression relation- ship between ASGR1 and DNMTs implies that ASGR1 may affect other genes’ expression lev- els or that ASGR1 may be regulated by DNMTs. Prior to this study, little was known about the mutation of ASGR1 in cancer, even though Nioi et al. [29] previously reported that a mutation (12-base-pair deletion) of ASGR1 could reduce the risk of coronary artery disease. In this study, some ASGR1 mutations occurred, such as mis- sense mutations and frameshift insertions in certain cancers, particularly for SKCM, STAD, and UCEC, which may contribute to the correla- tion of ASGR1 with MMRs, TMB, and MSI. The relevance of ASGR1 expression to neoantigen count was also determined in CHOL and PC- PG. Neoantigens, especially immune antigens, commonly trigger immune response activators [30-32], and the correlation between ASGR1 and neoantigens suggests that ASGR1 may be relevant to immune responses.
Targeting ASGR1 may be an essential strategy for the treatment of multiple cancers. Because immune cells can promote antiviral and antitu- mor biological processes, a decrease in imm- une cell levels can cause deterioration in patients with COVID-19 and cancer. For exam- ple, lymphocyte reduction and immunosup- pression are essential mechanisms leading to unfavorable prognoses in patients with COVID- 19 and cancer [33-35]. Zhang et al. [10] identi- fied a mild negative correlation between ASGR1 expression and the levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. These six types of immune cells play essential roles in tumorigenesis and development [36]. In this study, ASGR1 expression showed a significantly negative or positive relationship to infiltration levels for all six immune cells in CHOL, KIRP, and HNSCC. The close association between ASGR1 expres-
ASGR1 in human cancers
sion and the immune microenvironment in CHOL, DLBC, HNSCC, KICH, KIRP, and LAML was also confirmed via ESTIMATE scores. Notably, ASGR1 may play distinct roles in the immune environment in various cancers beca- use it was differently (positively or negatively) relevant to certain immune cells in specific cancers. Furthermore, ASGR1 expression was closely associated with ICPs in multiple can- cers, suggesting it may have the potential to act as a marker for use in the treatment of can- cers, consistent with some ICPs such as PD-1 [37, 38].
The mechanisms of ASGR1 in human cancers are complex and require further investigation. Based on the results of the GSEA, four KEGG signaling pathways (olfactory transduction, cytokine-cytokine receptor interaction, chemo- kine signaling pathway, and neuroactive ligand receptor interaction) were found in eight of 32 cancers, suggesting that ASGR1 may play its role in these cancers by affecting the four sig- naling pathways. For certain cancers (ACC, BRCA, BLCA, CHOL, KICH, KIRP, LAML, and TGCT), ASGR1 was observed to affect several KEGG signaling pathways. However, such find- ings should be confirmed by further experi- ments. Given that ASGR1 was determined to be a marker for treating multiple cancers, drugs that were potentially sensitive to ASGR1 were explored. As a result, 25 of 57 drug types were sensitive to ASGR1 based on IC50 values, which provides clues for further studies on drugs targeting ASGR1.
This study has several limitations. I failed to collect enough protein samples for a compre- hensive statistical analysis that would evaluate the ASGR1 protein level differences between cancer and non-cancer tissues. Although the potential of ASGR1 to identify cancer and non- cancer is evident, whether it can be used for direct screening of cancer still needs to be confirmed using fluid-related samples. In the future, sufficient in-house samples should be collected to conduct in vitro and in vivo experi- ments to validate the current results. Moreover, the common molecular mechanism of ASGR1 in human cancer and COVID-19 needs to be explored.
Conclusions
In all, this study has revealed that SARS-CoV-2- correlated ASGR1 is a novel marker for the
treatment and identification of multiple human cancers.
Acknowledgements
I acknowledge the reviewer for his/her detailed work and professional suggestions, which have significantly contributed to the improvement of my article. The results shown in the study are in part based upon data generated by the GTEx, CCLE, TCGA, HPA, and SangerBox (Version 3.0).
Disclosure of conflict of interest
None.
Address correspondence to: Dr. Tao Huang, Depart- ment of Cardiothoracic Vascular Surgery, The Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18 Zhongshan Second Road, Baise 533000, Guangxi Zhuang Autonomous Region, People’s Republic of China. E-mail: huang- tao_ymufm@163.com
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ASGR1 in human cancers
| Patient ID | Gender | Age (yr) | Cancer type | Group | Antibody | Staining intensity | Quantity | IHC score |
|---|---|---|---|---|---|---|---|---|
| 2330 | Female | 22 | Breast | Normal | HPA011954 | Negative | None | 0 |
| 2773 | Female | 23 | Breast | Normal | HPA011954 | Negative | None | 0 |
| 3544 | Female | 45 | Breast | Normal | HPA011954 | Negative | None | 0 |
| 1874 | Female | 80 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 1939 | Female | 87 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 2083 | Female | 51 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 2091 | Female | 40 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 2428 | Female | 75 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 2805 | Female | 59 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 2898 | Female | 41 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 3257 | Female | 39 | Breast | Tumor | HPA011954 | Negative | None | 0 |
| 1910 | Female | 61 | Breast | Tumor | HPA011954 | Weak | < 25% | 1 |
| 2174 | Female | 37 | Breast | Tumor | HPA011954 | Weak | < 25% | 1 |
| 2392 | Female | 27 | Breast | Tumor | HPA011954 | Weak | < 25% | 1 |
| 1423 | Female | 56 | COAD | Normal | HPA011954 | Negative | None | 0 |
| 2944 | Female | 80 | COAD | Normal | HPA011954 | Negative | None | 0 |
| 3266 | Male | 73 | COAD | Normal | HPA011954 | Negative | None | 0 |
| 1898 | Male | 71 | COAD | Tumor | HPA011954 | Negative | None | 0 |
| 1958 | Female | 84 | COAD | Tumor | HPA011954 | Negative | None | 0 |
| 2151 | Female | 75 | COAD | Tumor | HPA011954 | Negative | None | 0 |
| 2106 | Female | 86 | COAD | Tumor | HPA011954 | Weak | < 25% | 1 |
| 2616 | Female | 67 | COAD | Tumor | HPA011954 | Moderate | > 75% | 6 |
| 2948 | Female | 66 | COAD | Tumor | HPA011954 | Moderate | > 75% | 6 |
| 1711 | Male | 71 | HNSCC | Normal | HPA012852 | Weak | < 25% | 1 |
| 2484 | Male | 84 | HNSCC | Normal | HPA012852 | Weak | < 25% | 1 |
| 2547 | Male | 66 | HNSCC | Normal | HPA012852 | Weak | < 25% | 1 |
| 1743 | Male | 62 | HNSCC | Tumor | HPA012852 | Negative | None | 0 |
| 2547 | Male | 66 | HNSCC | Tumor | HPA012852 | Negative | None | 0 |
| 2608 | Male | 51 | HNSCC | Tumor | HPA012852 | Negative | None | 0 |
| 1767 | Male | 16 | Kidney | Normal | HPA012852 | Negative | None | 0 |
| 1933 | Female | 56 | Kidney | Normal | HPA012852 | Negative | None | 0 |
| 3229 | Male | 59 | Kidney | Normal | HPA012852 | Negative | None | 0 |
| 1498 | Female | 70 | Kidney | Tumor | HPA012852 | Negative | None | 0 |
| 1831 | Male | 77 | Kidney | Tumor | HPA012852 | Negative | None | 0 |
| 2067 | Female | 72 | Kidney | Tumor | HPA012852 | Negative | None | 0 |
| 2564 | Female | 52 | Kidney | Tumor | HPA012852 | Negative | None | 0 |
| 3225 | Male | 77 | Kidney | Tumor | HPA012852 | Negative | None | 0 |
| 3533 | Female | 54 | Kidney | Tumor | HPA012852 | Negative | None | 0 |
| 1752 | Male | 56 | Kidney | Tumor | HPA012852 | Weak | < 25% | 1 |
| 1901 | Female | 69 | Kidney | Tumor | HPA012852 | Weak | < 25% | 1 |
| 1969 | Male | 63 | Kidney | Tumor | HPA012852 | Weak | < 25% | 1 |
| 2176 | Male | 59 | Kidney | Tumor | HPA012852 | Weak | < 25% | 1 |
| 2452 | Male | 68 | Kidney | Tumor | HPA012852 | Weak | < 25% | 1 |
| 3061 | Female | 57 | Kidney | Tumor | HPA012852 | Weak | < 25% | 1 |
| 2429 | Male | 55 | LIHC | Normal | HPA011954 | Negative | None | 0 |
| 3222 | Female | 63 | LIHC | Normal | HPA011954 | Negative | None | 0 |
| 3402 | Female | 54 | LIHC | Normal | HPA011954 | Negative | None | 0 |
ASGR1 in human cancers
| 2177 | Female | 58 | LIHC | Tumor | HPA011954 | Moderate | 75%-25% | 4 |
|---|---|---|---|---|---|---|---|---|
| 3215 | Female | 61 | LIHC | Tumor | HPA011954 | Moderate | 75%-25% | 4 |
| 2766 | Female | 73 | LIHC | Tumor | HPA011954 | Moderate | > 75% | 6 |
| 3477 | Male | 67 | LIHC | Tumor | HPA011954 | Moderate | > 75% | 6 |
| 2280 | Male | 80 | LIHC | Tumor | HPA011954 | Strong | > 75% | 9 |
| 2556 | Male | 72 | LIHC | Tumor | HPA011954 | Strong | > 75% | 9 |
| 3196 | Male | 65 | LIHC | Tumor | HPA011954 | Strong | > 75% | 9 |
| 3346 | Female | 73 | LIHC | Tumor | HPA011954 | Strong | > 75% | 9 |
| 2101 | Male | 21 | LUAD | Normal | HPA012852 | Negative | None | 0 |
| 2222 | Male | 59 | LUAD | Normal | HPA012852 | Negative | None | 0 |
| 2268 | Female | 49 | LUAD | Normal | HPA012852 | Negative | None | 0 |
| 1847 | Male | 64 | LUAD | Tumor | HPA012852 | Negative | None | 0 |
| 2222 | Male | 59 | LUAD | Tumor | HPA012852 | Negative | None | 0 |
| 2403 | Female | 65 | LUAD | Tumor | HPA012852 | Negative | None | 0 |
| 3003 | Male | 49 | LUAD | Tumor | HPA012852 | Negative | None | 0 |
| 3052 | Female | 51 | LUAD | Tumor | HPA012852 | Negative | None | 0 |
| 2101 | Male | 21 | LUSC | Normal | HPA012852 | Negative | None | 0 |
| 2222 | Male | 59 | LUSC | Normal | HPA012852 | Negative | None | 0 |
| 2268 | Female | 49 | LUSC | Normal | HPA012852 | Negative | None | 0 |
| 1765 | Female | 63 | LUSC | Tumor | HPA012852 | Negative | None | 0 |
| 2100 | Female | 47 | LUSC | Tumor | HPA012852 | Negative | None | 0 |
| 2231 | Male | 71 | LUSC | Tumor | HPA012852 | Negative | None | 0 |
| 2268 | Female | 49 | LUSC | Tumor | HPA012852 | Negative | None | 0 |
| 2354 | Male | 61 | LUSC | Tumor | HPA012852 | Negative | None | 0 |
| 3016 | Female | 73 | LUSC | Tumor | HPA012852 | Negative | None | 0 |
| 2032 | Female | 35 | PAAD | Normal | HPA012852 | Negative | None | 0 |
| 2329 | Male | 66 | PAAD | Normal | HPA012852 | Negative | None | 0 |
| 3320 | Female | 70 | PAAD | Normal | HPA012852 | Negative | None | 0 |
| 823 | Female | 70 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 833 | Male | 74 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 2952 | Male | 41 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3004 | Female | 71 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3233 | Female | 56 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3363 | Female | 78 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3548 | Female | 60 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3591 | Female | 70 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3597 | Male | 53 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3599 | Male | 50 | PAAD | Tumor | HPA012852 | Negative | None | 0 |
| 3614 | Female | 61 | PAAD | Tumor | HPA012852 | Moderate | < 25% | 2 |
| 1904 | Male | 50 | PAAD | Tumor | HPA012852 | Strong | 75%-25% | 6 |
| 2053 | Male | 51 | PRAD | Normal | HPA011954 | Weak | < 25% | 1 |
| 2098 | Male | 60 | PRAD | Normal | HPA011954 | Weak | < 25% | 1 |
| 2932 | Male | 76 | PRAD | Normal | HPA011954 | Weak | < 25% | 1 |
| 2828 | Male | 89 | PRAD | Tumor | HPA011954 | Negative | None | 0 |
| 3486 | Male | 61 | PRAD | Tumor | HPA011954 | Negative | None | 0 |
| 3571 | Male | 71 | PRAD | Tumor | HPA011954 | Negative | None | 0 |
| 3578 | Male | 52 | PRAD | Tumor | HPA011954 | Negative | None | 0 |
| 3554 | Male | 57 | PRAD | Tumor | HPA011954 | Weak | < 25% | 1 |
| 3577 | Male | 60 | PRAD | Tumor | HPA011954 | Weak | < 25% | 1 |
ASGR1 in human cancers
| 3559 | Male | 50 | PRAD | Tumor | HPA011954 | Moderate | < 25% | 2 |
|---|---|---|---|---|---|---|---|---|
| 3572 | Male | 65 | PRAD | Tumor | HPA011954 | Moderate | < 25% | 2 |
| 3573 | Male | 63 | PRAD | Tumor | HPA011954 | Weak | 75%-25% | 2 |
| 3579 | Male | 61 | PRAD | Tumor | HPA011954 | Moderate | < 25% | 2 |
| 3580 | Male | 69 | PRAD | Tumor | HPA011954 | Moderate | < 25% | 2 |
| 2953 | Male | 64 | READ | Normal | HPA011954 | Moderate | < 25% | 2 |
| 3231 | Male | 44 | READ | Normal | HPA011954 | Moderate | < 25% | 2 |
| 3243 | Female | 65 | READ | Normal | HPA011954 | Moderate | < 25% | 2 |
| 2001 | Male | 92 | READ | Tumor | HPA011954 | Negative | None | 0 |
| 2060 | Female | 66 | READ | Tumor | HPA011954 | Weak | < 25% | 1 |
| 3408 | Male | 63 | READ | Tumor | HPA011954 | Moderate | < 25% | 2 |
| 3074 | Male | 72 | READ | Tumor | HPA011954 | Weak | > 75% | 3 |
| 3274 | Female | 89 | READ | Tumor | HPA011954 | Moderate | > 75% | 6 |
| 2473 | Male | 59 | STAD | Tumor | HPA011954 | Negative | None | 0 |
| 2959 | Female | 59 | STAD | Tumor | HPA011954 | Negative | None | 0 |
| 464 | Male | 69 | STAD | Tumor | HPA011954 | Weak | < 25% | 1 |
| 1787 | Male | 82 | STAD | Tumor | HPA011954 | Weak | < 25% | 1 |
| 2105 | Male | 62 | STAD | Tumor | HPA011954 | Weak | < 25% | 1 |
| 2195 | Male | 48 | STAD | Tumor | HPA011954 | Weak | < 25% | 1 |
| 2142 | Male | 62 | STAD | Tumor | HPA011954 | Moderate | < 25% | 2 |
| 2066 | Male | 76 | STAD | Tumor | HPA011954 | Moderate | < 25% | 2 |
| 2557 | Female | 73 | STAD | Tumor | HPA011954 | Moderate | < 25% | 2 |
| 2130 | Female | 56 | STAD | Normal | HPA011954 | Strong | 75%-25% | 6 |
| 3368 | Male | 39 | STAD | Normal | HPA011954 | Strong | 75%-25% | 6 |
| 2583 | Male | 72 | STAD | Normal | HPA011954 | Strong | 75%-25% | 6 |
| 2378 | Male | 59 | STAD | Tumor | HPA011954 | Moderate | > 75% | 6 |
| 3270 | Female | 89 | STAD | Tumor | HPA011954 | Moderate | > 75% | 6 |
| 1672 | Male | 56 | THCA | Normal | HPA011954 | Negative | None | 0 |
| 3005 | Female | 44 | THCA | Normal | HPA011954 | Negative | None | 0 |
| 3536 | Female | 28 | THCA | Normal | HPA011954 | Negative | None | 0 |
| 2623 | Male | 77 | THCA | Tumor | HPA011954 | Negative | None | 0 |
| 3107 | Male | 75 | THCA | Tumor | HPA011954 | Negative | None | 0 |
| 3267 | Male | 33 | THCA | Tumor | HPA011954 | Negative | None | 0 |
| 3490 | Female | 42 | THCA | Tumor | HPA011954 | Negative | None | 0 |
| 2242 | Female | 42 | UCEC | Normal | HPA011954 | Negative | None | 0 |
| 2941 | Female | 33 | UCEC | Normal | HPA011954 | Negative | None | 0 |
| 3313 | Female | 39 | UCEC | Normal | HPA011954 | Negative | None | 0 |
| 1881 | Female | 86 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 1766 | Female | 53 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 2118 | Female | 70 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 2339 | Female | 79 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 2455 | Female | 58 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 2607 | Female | 81 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 2621 | Female | 58 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 3036 | Female | 32 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 3319 | Female | 85 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 3367 | Female | 70 | UCEC | Tumor | HPA011954 | Negative | None | 0 |
| 1881 | Female | 86 | UCEC | Tumor | HPA011954 | Weak | < 25% | 1 |
| 2772 | Female | 63 | UCEC | Tumor | HPA011954 | Weak | < 25% | 1 |
ASGR1 in human cancers
| Immunohistochemical staining features | Score | |
|---|---|---|
| Staining intensity | Negative | 0 |
| Weak | 1 | |
| Moderate | 2 | |
| Strong | 3 | |
| Quantity | None | 0 |
| < 25% | 1 | |
| 25%-75% | 2 | |
| > 75% | 3 | |
BRCA
CHOL
COAD
ESCA
KICH
KIRP
0.56
0.5
0.058
0.28
0.68
0.088
8
0.5
12.5
0.68
8
03
0.75
0,33
5
0.5
0.93
0.2
0.45
0.15
4
0.61
0.54
0,51
10.0
0.73
0.2
7.5
0.98
0,86
0,19
ASGR1 expression
0.87
ASGR1 expression
0.4
ASGR1 expression
0.93
0,78
0.29
5 6
0,63
ASGR1 expression
0.16
0,51
ASGR1 expression
0.5
4
0.17
0.11
ASGR1 expression
3
0.89
7.5
5.0
3
4
4
2
5.0
2
2
…
2
2.5
1
2.5
1
0
F
4
0
0.0
0
0
Stage | Stage II Stage III Stage IV AJCC_stage (n = 1067 )
Stage | Stage IIStage IIIStage IV AJCC_stage (n = 36 )
Stage
Stage II Stage III Stage IV AJCC_stage (n = 276 )
Stage | Stage II Stage IIIStage IV AJCC_stage (n = 158 )
Stage
Stage II Stage III Stage IV AJCC_stage (n = 66 )
Stage | Stage II Stage III Stage IV AJCC_stage (n = 258 )
LUAD
LUSC
READ
SKCM
STAD
UVM
10.0
0.91
0.29
8
0.89
0.45
0.76
0,38
0.76
0.17
0.74
0 35
0.84
0.71
0.26
0.59
0.59
0.12
0.4
0.19
0.17
0.21
7.5
0.8
7.5
0.76
0.78
ASGR1 expression
7.5
0.29
0.52
ASGR1 expression
0.5
6
0.59
ASGR1 expression
0.059
0.14
ASGR1 expression
4
0.3
0.35
ASGR1 expression
0.6
ASGR1 expression
0.9
5.0
5.0
4
5.0
2.
1
2.5
2.5
2
2.5
210
:
0.0
0.0
A
0
0
0.0
0
Stage
I Stage II Stage IIIStage IV AJCC_stage (n = 505 )
Stage | Stage II Stage IIIStage IV AJCC_stage (n = 494 )
Stage
Stage II Stage III Stage IV AJCC_stage (n = 82 )
Stage | Stage II Stage III Stage IV AJCC_stage (n = 97 )
Stage | Stage II Stage IIIStage IV AJCC_stage (n = 389 )
Stage II
Stage III AJCC_stage (n = 78 )
Stage IV
ASGR1 in human cancers
ACC
BRCA
CHOL
COAD
DLBC
ESCA
0.062
0.14
8
0.53
0.85
0.83
0.55
3
6-
9
.
4
ASGR1 expression
ASGR1 expression
4
ASGR1 expression
6
ASGR1 expression
4
ASGR1 expression
ASGR1 expression
4
4
2
2
2
4
.
2
2
1
1
2
2
.
0
0
0
<65
=65
<85
=65
<65
=65
<85
=85
<65
=65
<85
=85
Age_in_year (n = 77 )
Age_in_year (n = 1090 )
Age_in_year (n = 36 )
Age_in_year (n = 286 )
Age_in_year (n = 47 )
Age_in_year (n = 181 )
GBM
HNSCC
KICH
KIRC
KIRP
LIHC
0.057
5
0.38
3
0.27
0.53
6
0.3
0.59
4
6
·
:
ASGR1 expression
4
9
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
4
3
3
4
6
2
1
2
2
2
1
3
1
0
0
0
0
Age_in_year (n = 152 )
<65
=65
$65
=65
Age_in_year (n = 517 )
Age_in_year (n = 66 )
<65
=65
Age_in_year (n = 530 )
<65
=65
=65
Age_in_year (n = 285 )
<65
=65
Age_in_year (n = 368 )
<65
LUAD
LUSC
MESO
OV
PAAD
PCPG
0.9
6-
0,13
0.79
0.19
0.55
0.96
6
3
.
6
4
ASGR1 expression
ASGR1 expression
ASGR1 expression
2
ASGR1 expression
ASGR1 expression
ASGR1 expression
4
4
3
2
4
#
1
2
2
2
1
2
1
0
<65
=65
0
0
-65
=65
<65
=65
<65
0
Age_in_year (n = 489 )
Age_in_year (n = 419 )
=65
=65
Age_in_year (n = 494 )
Age_in_year (n = 87 )
Age_in_year (n = 178 )
<65
=65
Age_in_year (n = 177 )
<65
PRAD
READ
SARC
SKCM
STAD
TGCT
5
0.17
0.82
4
0.33
0.16
5
6-
0.55
6-
0.39
₹
ASGR1 expression
4
4
3
3
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
4
ASGR1 expression
4
&
3
2
V
2
.
2
2
2
1
1
1
1
0
<65
=65
<85
=65
0
<65
=65
0
:
0
485
85
Age_in_year (n = 495 )
Age_in_year (n = 91 )
Age_in_year (n = 102 )
Age_in_year (n = 409 )
<65
=65
Age_in_year (n = 258 )
Age_in_year (n = 132 )
<85
=85
THYM
UCEC
UCS
UVM
0.82
6-
0.62
0.89
0.56
3
4
·
2.0
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
4
3
1.5
2
*
1.0
2
1
2
.
0.5
1
0
0.0
<65
=65
Age_in_year (n = 177 )
<65
Age_in_year (n = 118 )
=65
Age_in_year (n = 57 )
<65
=65
Age_in_year (n = 79 )
<65
=65
ASGR1 in human cancers
BLCA
BRCA
CHOL
COAD
DLBC
ESCA
5
0.1
0.13
8
0.15
0.29
0.39
6
0.24
3
ASGR1 expression
4
ASGR1 expression
4
ASGR1 expression
6
ASGR1 expression
4
ASGR1 expression
4
2
9
ASGR1 expression
A3
:
:
·
2
2
4
2
·
1
.
1
·
2
·
.
0
0
0
0
Female
Gender (n = 407 )
Male
Female
Male
Gender (n = 1091 )
Female
Male
Female
Female
Female
Male
Gender (n = 36 )
Gender (n = 286 )
Male
Gender (n = 47 )
Male
Gender (n = 181 )
GBM
HNSCC
KICH
KIRC
KIRP
LAML
0.84
5
0.33
3
0.45
0.83
6-
0.63
4
0.73
4
6
ASGR1 expression
4
1
ASGR1 expression
ASGR1 expression
2
ASGR1 expression
ASGR1 expression
ASGR1 expression
: 3
4
4
3
4
2
2
1
2
2
2
1
1
?
1
0
0
0
0
Male
0
Female
Male
Gender (n = 152 )
Female
Gender (n = 518 )
Female
Gender (n = 66 )
Male
Female
Gender (n = 530 )
Male
Female
Gender (n = 288 )
Male
Female
Gender (n = 173 )
Male
LGG
LIHC
LUAD
LUSC
MESO
PAAD
6
0.083
0.53
0,46
:
6
0.49
0.95
0.4
6
3
6
9
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
4
4
4
4
6
2
2
2
2.
2
3
1
:
Female
0
Male
Female
Male
0
Gender (n = 508 )
Gender (n = 369 )
Female
Gender (n = 513 )
Male
Female
Male
Gender (n = 498 )
Female
Gender (n = 87 )
Male
0
Female
Gender (n = 178 )
Male
PCPG
READ
SARC
SKCM
STAD
THCA
0.33
0.86
4
0.78
0.38
0.14
0.2
4
5
6-
6
4
3
3
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
ASGR1 expression
50
4
ASGR1 expression
4
3
2
N
2
2
2
2
1
1
1
1
*
0
·
0
Female
Gender (n = 177 )
Male
Female
Gender (n = 91 )
Male
Female
Gender (n = 258 )
Male
0
Female
Gender (n = 102 )
Male
Female
Gender (n = 414 )
Male
0
Female Gender (n = 504 )
Male
THYM
UVM
0.063
0.86
3
2.0
ASGR1 expression
ASGR1 expression
1.5
0
=
1.0
1
0.5
.
0.0
Female
Gender (n = 119 )
Male
Female
Gender (n = 79 )
Male
ASGR1 in human cancers
ASGR1 Log2(TPM+1)
ACC (n = 77)
ASGR1 Log2(TPM+1)
ACC (n = 77)
ASGR1 Log2(TPM+1)
ACC (n = 77)
ASGR1 Log2(TPM+1)
ACC (n = 77)
ASGR1 Log2(TPM+1)
ACC (n = 77)
ASGR1 Log2(TPM+1)
ACC (n = 77)
ASGR1 Log2(TPM+1)
BLCA (n = 406)
ASGR1 Log2(TPM+1)
BLCA (n = 406)
ASGR1 Log2(TPM+1)
BLCA (n = 406)
ASGR1 Log2(TPM+1)
BLCA (n = 406)
BLCA (n = 406)
ASGR1 Log2(TPM+1)
BLCA (n = 406)
P =- U.14, 0
123
-U.S. P = U.UUZ]
-U.23, p = 0.UT
-UN5, p = U.UUTy
1 p . U.18. p = U.UUUSO
1
p .U.15, p = 0.21
+U.14. P =U.UUST
ASGR1 Log2(TPM+1)
+ 0.33, p = 8.18-12
U.21, P = U.UUJUT
0.11 0.12 0.13 B_cell level
0.09 0.11 0.13 0.15
0.20
0.25 0.30 0.35
0.12 0.14 0.16 0.18 Neutrophil level
0.08 0.12 0.16 Macrophage level
0.49 0.50 0.51 0.52 0.53 Dendritic level
0.0
0.5 1.0 1.5
5
2.0
0.0 0.2 0.4 0.6
0.0 0.2 04 0.6 08 CD8_Tcell level
0.0 02 0.4 0.6
CD4_Tcell level
0.4 0.8 1.2 1 Dendritic level
1.6
CDB_Tcell level
B_cell level
CD4_Tcell level
0.1 0.2 0.3 0.4 Neutrophil level
Macrophage level
ASGR1 Log2(TPM+1)
BRCA (n = 1088)
ASGR1 Log2(TPM+1)
BRCA (n = 1088)
ASGR1 Log2(TPM+1)
BRCA (n = 1088)
ASGR1 Log2(TPM+1)
BRCA (n = 1088)
ASGR1 Log2(TPM+1)
BRCA (n = 1088)
ASGR1 Log2(TPM+1)
BRCA (n = 1088)
ASGR1 Log2(TPM+1)
COAD (n = 288)
ASGR1 Log2(TPM+1)
COAD (n = 288)
ASGR1 Log2(TPM+1)
COAD (n = 288)
ASGR1 Log2(TPM+1)
COAD (n = 288)
ASGR1 Log2(TPM+1)
COAD (n = 288)
ASGR1 Log2(TPM+1)
COAD (n = 288)
-[ =0.10.p = 2.16 Uy
!
P U.45, p < 2.20 10
P. U.10, 0 5.18 Ud
A
P& U. 36, p < 2.20 10
U.13, D = U.UUUUZ
P .U.43, 0 5 2.20 10
-U.16, p .U.UUOD
U.UID. P.U./b
18. P. U.UU24
PS-V.12, p = U.USS
U.UZT. P =U.05
U. 10. p. U.UUd
%
0.0
05 1.0 1.5 20 B_cell level
0.0 0.5 10 1.5 2.0
0.0
0.5 1.0
0.00 0.25 0.50 0.75
0.0 0.3 0.6 0.9 Macrophage level
2
0.0
02 0.4 0.6
0.8
0.0 02 0.4 0.6 0.0
0.0 0.2
0.4
0.6
0.0
02 04 06 Neutrophil level
0.0 0.2 0.4 0.6 Macrophage level
0.4 08 12 16 Dendritic level
CD4_Tcell level
CD8_Tcell level
Neutrophil level
Dendritic level
B_cell level
CD4_Tcell level
CDB_Toell level
ASGR1 Log2(TPM+1)
DLBC (n = 27)
ASGR1 Log2(TPM+1)
DLBC (n = 27)
ASGR1 Log2(TPM+1)
DLBC (n = 27)
ASGR1 Log2(TPM+1)
DLBC (n = 27)
ASGR1 Log2(TPM+1)
DLBC (n = 27)
ASGR1 Log2(TPM+1)
DLBC (n = 27)
ESCA (n = 181)
ESCA (n = 181)
ESCA (n = 181)
ESCA (n = 181)
ESCA (n = 181)
ESCA (n = 181)
U.S.T. P .U.UOD
P =0.029, p = 0.00
PUSS, PROUT9
P = 0.0, P = U.ULUOD
ASGR1 Log2(TPM+1)
3.10. P = U.UTO
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
1.0.20, p =0.19
00.22. PU.DUZO
PE UND1, 0 044
00:1, 92022
1
A
-
-
N
·
0.0
02
0.4
0.6
0.0 01 02 0.3 0.4 05 CD4_Tcell level
0.10 0.16 0.20 0.25 CD8_Tcell level
0.1
0.2
0.3
1.00 0.05 0.10 0.15 Macrophage level
0.3
3 0.4 0.5 0.6 0.7 Dendritic level
0.2 0.4 0 0.6
0.2
0.4
0.6
0.10 0.15 0.20 0.25 0.30 CDB_Tcell level
0.0750.1000.1250.1600.175 Neutrophil level
0.00 0.05 0.10 0.15 Macrophage level
0.5
0.6 Dendritic level
0.7
B_cell level
Neutrophil level
B_cell level
CD4_Tcell level
ASGR1 Log2(TPM+1)
GBM (n = 150)
GBM (n = 150)
-U.049. P = 0,05
ASGR1 Log2(TPM+1)
-Que2, P=0.32
ASGR1 Log2(TPM+1)
GBM (n = 150)
ASGR1 Log2(TPM+1)
GBM (n = 150)
p=0.11, p=0.17
- - U. 14. p = 0.US9
ASGR1 Log2(TPM+1)
GBM (n = 150)
GBM (n = 150)
KICH (n = 66)
-0.094, P=031
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
= U. 10. p = 0.19
ASGR1 Log2(TPM+1)
KICH (n = 66)
= 0.48. PEU.VOUS
ASGR1 Log2(TPM+1)
KICH (n = 66)
ASGR1 Log2(TPM+1)
KICH (n = 66)
ASGR1 Log2(TPM+1)
KICH (n = 66)
KICH (n = 66)
-U.U.A. p=U.ry
P=U.I. P=0.41
20.30. PEU0033
ASGR1 Log2(TPM+1)
0 = 0.41, p= 0.00000
4
2
4
N
9
L
0.00 0.25 0.50 0,75 B_cell level
0.0
0.2
0.6 CD4_Tcell level
0.4
0.0 03 0.6 09 1.2 CD8_Tcell level
0.0 0.3 0.6 0.9 Neutrophil level
1.2
00 02
0.4
5
Dendritic level
0.08 0.10 0.12 0.14 B_cell level
0.08 0.12 0.16 0.20 0.24 CD4_Tcell level
0.10 0.15 0.20 0.25
0.10 0.12 Neutrophil level
0,14
Macrophage level
0.00 0.05 0.10 0,15 0.20 Macrophage level
0.45 0.50 0.55 0.60 Dendritic level
CD8_Tcell level
ASGR1 Log2(TPM+1)
KIRC (n = 530)
ASGR1 Log2(TPM+1)
KIRC (n = 530)
ASGR1 Log2(TPM+1)
KIRC (n = 530)
ASGR1 Log2(TPM+1)
KIRC (n = 530)
KIRC (n = 530)
KIRC (n = 530)
LGG (n = 509)
=0.14, p = 0.0014
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
LGG (n = 509)
ASGR1 Log2(TPM+1)
LGG (n = 509)
ASGR1 Log2(TPM+1)
LGG (n = 509)
ASGR1 Log2(TPM+1)
LGG (n = 509)
ASGR1 Log2(TPM+1)
LGG (n = 509)
-
.
p=0.34.p=5.60-70
=0.25, 0 = 1.40-08
= 0.36, p < 2.28-16
= 0.24, 0= 3.20-08
= 0.37, p < 2.28-10
D = - 0.33, p = 3.40-
p == 0.25, p=240-1
=- U.28. p = 1.20-
P == 0.34, p =5.50-
P =- U.41, 0 < 228-
-0.30, p < 2.20-
0
A
9
Y
A
A
0.4
0.0 0.2 0.4 0.8 0.8
2
0.0
02
0.6
0.0 0.5 10
.5
0.0 0.1 0.2 03 04 05 Neutrophil level
2
1 CD8_Tcell level
0.0 0.2 0.4 0.6 0.8 Macrophage level
0.0 05 10 15 20 Dendritic level
00 01 0.2 03 04 B_cell level
0 02 04 06 08 CD4_Tcell level
01 02 03 04 05 06 CDB_Tcell level
0.1 0.2 0.3 04 Neutrophil level
0.0
00 03 06 0.9 Macrophage level
0.4 0.8 12 1.6 Dendritic level
B_cell level
CD4_Tcell level
ASGR1 Log2(TPM+1)
LIHC (n = 369)
ASGR1 Log2(TPM+1)
LIHC (n = 369)
LIHC (n = 369)
LIHC (n = 369)
LIHC (n = 369)
LIHC (n = 369)
LUAD (n = 508)
LUAD (n = 508)
LUAD (n = 508)
LUAD (n = 508)
-V.2, p = U.VUL
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
-U.31. p = b.be
ASGR1 Log2(TPM+1)
P = 0.034, p = 0.055
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
p =- U.vor, p = 0.13
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
LUAD (n = 508)
ASGR1 Log2(TPM+1)
LUAD (n = 508)
10.0
10.0-
10.0-
0.0-
10.0-
p = 0.16, p = 0.UUU95
P = U. 13. P = U.UUOUT
3
K
8
N
&
en
·
6
TO
O
,
0
5.0
A
5.0
9
4
25
A
25
2
0.0
0.5
1.0
0.0
0.5
1.0
0.0 0.3 0.6 0.9
0.05 0.10 0.15 0.20 0.25 Neutrophil level
0.00 0.25 0.50 0.75 1.00 Macrophage level
0.5 10 15 Dendritic level
0.00 0.25 0.50 0.75
e 0.0
0.2 0.4 0.6 0
0.8
0.00 0.25 0.50 0.75
0.0 0.1 02 03 04 05 Neutrophil level
0.0 0.2 04 0.6 0 Macrophage level
0.8
00
0.5
1.5
B_cell level
CD4_Tcell level
CD8_Tcell level
B_cell level
CD4_Tcell level
CD8_Tcell level
Dendritic level
ASGR1 Log2(TPM+1)
LUSC (n = 498)
ASGR1 Log2(TPM+1)
LUSC (n = 498)
ASGR1 Log2(TPM+1)
LUSC (n = 498)
ASGR1 Log2(TPM+1)
LUSC (n = 498)
LUSC (n = 498)
LUSC (n = 498)
MESO (n = 86)
MESO (n = 86)
MESO (n = 86)
MESO (n = 86)
MESO (n = 86)
MESO (n = 86)
P. U. Tb. P .U.UUU25
P. U.31, p . 1.28-12
P. U.14. P . U.UUTS
P. U. 16, P U.UU020
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
p .U.23. p. 3.28
ASGR1 Log2(TPM+1)
P. U.S2, P. J.He 13
ASGR1 Log2(TPM+1)
P Uzb. p _0.015
ASGR1 Log2(TPM+1)
P. U.Z. p .U.UUUU
ASGR1 Log2(TPM+1)
OU.UTA, P .U.S
ASGR1 Log2(TPM+1)
P .u.14, pruz
ASGR1 Log2(TPM+1)
P. U.21. P .U.UTS
P =0.44.p .26 U5
K
0
S
9
.
4
9
P
9
9
2
®
A
.
5
1
6
0.0
0.5 1.0 1.5 20 B_cell level
(
0.0
D
0.5
1.0
1.5
0.0 02 0.4 0.6 0.8 CD8_Tcell level
0.0 0.1 02 03 04 05 Neutrophil level
0.0 0.1 0.2 0.3 0.4 Macrophage level
0.5
1.0
0.1
0.2
0.3
0.1
0.2
0.3
0.0
0.2
0.4
0.100 0.125 0.150 0.175 Neutrophil level
0.00 0.05 0.10 0.15 Macrophage level
0.3 0.4 0.5 0.6 0.7 Dendritic level
CD4_Tcell level
Dendritic level
B_cell level
CD4_Toell level
CD8_Tcell level
ASGR1 Log2(TPM+1) 7 .
OV (n = 409)
ASGR1 Log2(TPM+1)
OV (n = 409)
ASGR1 Log2(TPM+1)
OV (n = 409)
ASGR1 Log2(TPM+1)
OV (n = 409)
ASGR1 Log2(TPM+1)
OV (n = 409)
ASGR1 Log2(TPM+1)
OV (n = 409)
ASGR1 Log2(TPM+1)
PAAD (n = 178)
ASGR1 Log2(TPM+1)
PAAD (n = 178)
ASGR1 Log2(TPM+1)
PAAD (n = 178)
ASGR1 Log2(TPM+1)
PAAD (n = 178)
ASGR1 Log2(TPM+1)
PAAD (n = 178)
ASGR1 Log2(TPM+1)
PAAD (n = 178)
PLEU. 12. P 0019
P =0.29, 2 -220-09
P = 0,099, P _ 0.40
P .U. TO. P _U.00029
p 10.20. P _ 1,40 04
2 -0.19, p =0.00009
PLEU. 14. 0 0.000
P _U.S. P - 1.Te
PLUS. PUOI
P =0.24, PRODUITS
P .D. T.P . U.U24
A
5
4
4
A
O
.
9
a
A
A
L
C
0.12 0.16 0.20 CD4_Tcell level
C
0.05
0.10
0.1
02
0.3
0.05
5 0.10 0.15 0.20 Neutrophil level
0.00
0.05 900 0.10 Macrophage level
0.4 0.5 0.6 0.7 Dendritic level
0.0
02 04 06 0.8 B_cell level
02
0.4
06
0.1 0.2 0.3 04 CD8_Tcell level
0.05
0.6
08
CD8_Tcell level
CD4_Tcell level
0.10 0.16 0.20 Neutrophil level
0.0 01 02 03 Macrophage level
02
0.4
B_cell level
Dendritic level
ASGR1 in human cancers
ASGR1 Log2(TPM+1)
PCPG (n = 177)
ASGR1 Log2(TPM+1)
PCPG (n = 177)
ASGR1 Log2(TPM+1)
PCPG (n = 177)
ASGR1 Log2(TPM+1)
PCPG (n = 177)
ASGR1 Log2(TPM+1)
PCPG (n = 177)
ASGR1 Log2(TPM+1)
PCPG (n = 177)
PRAD (n = 495)
PRAD (n = 495)
PRAD (n = 495)
PRAD (n = 495)
PRAD (n = 495)
PRAD (n = 495)
p= 029,P=0.002
P=0AS, P =300-US
P = 0.024, P = U.rb
P =U.U20, P =U. IS
P = U.S. P = D.00-U
ASGR1 Log2(TPM+1)
P = U.U89, P = U.UST
ASGR1 Log2(TPM+1)
p =0.51, p=49-12
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1) /
P=0.22, p=0.10-01
ASGR1 Log2(TPM+1)
P = 0, 14, p = U.UU4
P ASGR1 Log2(TPM+1) / S
p =U.Z1,P = 2.60-UC
4
0
5
5
O
TO
0
”
·
1
0.00 0.05 0.10 0.15 0.20
0.10 0.15 0 20 0.25 0.30
0.15 0.20 0.25 0.30
CD4_Tcell level
CD8_Tcell level
0,10 0.15 0.20 0.25 Neutrophil level
0.0 0.1 02 0.3 04 0.5
Macrophage level
0.3 0.4 0.5 0.6 0.7 Dendritic level
0.00 0.25 0.50 0.75 1.00 B_cell level
0.0
03 06 09 CD4_Tcell level
0.0 0.1 0.2 0.3 04 05 CD8_Tcell level
0.2
0.4
0.6
0.0 0.1 0.2 0.3 04 Macrophage level
0.0
0.5 1.0 15
2.0
B_cell level
Neutrophil level
Dendritic level
ASGR1 Log2(TPM+1)
READ (n = 92)
ASGR1 Log2(TPM+1)
READ (n = 92)
ASGR1 Log2(TPM+1)
READ (n = 92)
ASGR1 Log2(TPM+1)
READ (n = 92)
ASGR1 Log2(TPM+1)
READ (n = 92)
ASGR1 Log2(TPM+1)
READ (n = 92)
-0.06, p = 0.07
p =- UU61. p = 0.56
— U.U14, P = 0.65
p = 0.02, p = 0.85
p = 0.046. p = 0.66
-U.W60. p = 0.93
ASGR1 Log2(TPM+1)
SARC (n = 256)
SARC (n = 256)
P = U.I. p = U.Usb
ASGR1 Log2(TPM+1)
p=0.25, p= 240-00
ASGR1 Log2(TPM+1)
SARC (n = 256)
2 ASGR1 Log2(TPM+1) ASORTE
SARC (n = 256)
ASGR1 Log2(TPM+1)
SARC (n = 256)
ASGR1 Log2(TPM+1)
SARC (n = 256)
P =U.Z. p=U.UUT
p=0.25, p = 0.00020
p = 0.22, 0 = 0.0004
8
2
8
UL
®
m
9
4
×
NO
C
0.1 0.2 0.3 0.4 B_cell level
0.12 0.16 0.20
0.15 0.20 0.25 0.30
0.08
CD4_Tcell level
CD8_Tcell level
8 0.12 0.16 0.20
0.0
0.1
0.2
0.3 0.4 0.5 0.6 0.7 0.8
0.00 0.25 0.50 0.75 1.00 1.25 B_cell level
0.00 0.25 0.50 0.75 1.00
0.00 0.25 0.50 0.75
0.05
0.10 0.15 0.20 0.25 Neutrophil level
0.0
Neutrophil level
Macrophage level
Dendritic level
CD4_Tcell level
CD8_Tcell level
02 0.4 0.6 Macrophage level
0.25 0.50 0.75 1.00 1.25 Dendritic level
ASGR1 Log2(TPM+1)
SKCM (n = 101)
ASGR1 Log2(TPM+1)
SKCM (n = 101)
ASGR1 Log2(TPM+1)
SKCM (n = 101)
ASGR1 Log2(TPM+1)
SKCM (n = 101)
ASGR1 Log2(TPM+1)
SKCM (n = 101)
P .U. 13. P.U.19
p = U.20, 0 - U.UUDT
P - U.41, P - U.UUUVT
- 0.44, P = 3.be-UD
ASGR1 Log2(TPM+1)
SKCM (n = 101)
ASGR1 Log2(TPM+1)
STAD (n = 403)
P == U.21. p=0.0000
ASGR1 Log2(TPM+1)
STAD (n = 403)
STAD (n = 403)
P -U.UCB, P = U.UIT
ASGR1 Log2(TPM+1)
STAD (n = 403)
-
- U.VID, P = V.T.
ASGR1 Log2(TPM+1)
P =U.UST. p = U.UST
ASGR1 Log2(TPM+1)
STAD (n = 403)
STAD (n = 403)
9 - 0. 12, P = 0.014
ASGR1 Log2(TPM+1)
P =U.VSS, P = ULUTO
8
0
4
10
9
₼
A
0.0 0.1 0.2 0.3 0.4
A
.
V
0.0 0.1 0.2 0.3 0.4 B_cell level
0.0 0.1 0.2 0.3 0.4 CD8_Tcell level
0.0
0.1
0.2
2
0.3
0.00 0.05 0.10 0.15 Macrophage level
0.2
0.4
0.6
0.0
0.0 0.2 0.4 0.6 0.8
0.1
CD4_Tcell level
0.00 0.25 0.50 0.75 1.00 B_cell level
0.00 0.25 0.50 0.75 1.00
0.2
0.3
0.0 0.1 0.2 0.3 0.4 Macrophage level
0.4 0.6 0.8 Dendritic level
.0
Neutrophil level
Dendritic level
CD4_Tcell level
CD8_Tcell level
Neutrophil level
ASGR1 Log2(TPM+1)
TGCT (n = 148)
ASGR1 Log2(TPM+1)
TGCT (n = 148)
ASGR1 Log2(TPM+1)
TGCT (n = 148)
ASGR1 Log2(TPM+1)
TGCT (n = 148)
ASGR1 Log2(TPM+1)
TGCT (n = 148)
ASGR1 Log2(TPM+1)
TGCT (n = 148)
ASGR1 Log2(TPM+1)
THCA (n = 500)
THCA (n = 500)
ASGR1 Log2(TPM+1)
THCA (n = 500)
THCA (n = 500)
ASGR1 Log2(TPM+1)
THCA (n = 500)
ASGR1 Log2(TPM+1)
THCA (n = 500)
P-0.14, P .0.1
P-0.40, p =0.00
-0.24. P =0.0030
U. 15, P _U.U20
-0.29, 00.00US
ASGR1 Log2(TPM+1)
-0.14. P0.0010
ASGR1 Log2(TPM+1)
PLUS, P 9.78-12
U.S.p.1.le
P 0.54, P . 1.25
P-0.24. p. s.be
PUzo, p Sze
40
5
1
4
A
0
00
S
0
4
0.0 0.1 02 0.3
0.1 02 03 04 05
0.1 0.2 0.3 0.
0.10 0.15 0.20 0.25
0.00 0.05 0.10 0.15 0.20
AL
0.4
0.5
00 02 04 0.6
1
0.8
0.0 0.1 02 0.3 0.4 0.5
0.00 0.25 0.50 0.75 1.00
0.1
02
0.3
0.0
0.1 0.2
0.3
0.4
0.8
12
1.6
B_cell level
CD4_Tcell level
CD8_Tcell level
Neutrophil level
Macrophage level
Dendritic level
B_cell level
CD4_Tcell level
CD8_Tcell level
Neutrophil level
Macrophage level
Dendritic level
ASGR1 Log2(TPM+1)
THYM (n = 118)
THYM (n = 118)
THYM (n = 118)
THYM (n = 118)
THYM (n = 118)
= = 0.34, P =U.000
ASGR1 Log2(TPM+1)
P = U. I. P =U 24
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
P=U.I. P=U25
ASGR1 Log2(TPM+1)
= 0. 15. P = 0.054
ASGR1 Log2(TPM+1)
THYM (n = 118)
ASGR1 Log2(TPM+1)
UCEC (n = 180)
ASGR1 Log2(TPM+1)
UCEC (n = 180)
ASGR1 Log2(TPM+1)
UCEC (n = 180)
ASGR1 Log2(TPM+1)
UCEC (n = 180)
ASGR1 Log2(TPM+1)
UCEC (n = 180)
ASGR1 Log2(TPM+1)
UCEC (n = 180)
P =U. TD, P = UUSZ
- U.U26, p = D.IT
0
A
.
140.TD, P=0043
p = U. IO, P = U.US
A
E
O
9
en
A
9
9
@
A
0
0.0
0.1
02
0.3
0.0
0.2
0.4
0.0 0.1 02 03
S
0.04 0.08 0.12 0.16 Neutrophil level
0.00 0.05 0.10 0.15 0 20 0.25 Macrophage level
0
0.6
0.8
0.0 0.2 04 06
0.0
0.2
0,4
00 0.5 10 15
CD8_Tcell level
0.05 0.10 0.15 0.20 0.25 Neutrophil level
CD4_Tcell level
CD8_Tcell level
0.0 01 02 03 0 Macrophage level
0.4
0.25 0.50 0.75 1.00 1.25
B_cell level
Dendritic level
B_cell level
CD4_Tcell level
Dendritic level
ASGR1 Log2(TPM+1)
UCS (n = 57)
ASGR1 Log2(TPM+1)
UCS (n = 57)
ASGR1 Log2(TPM+1)
UCS (n = 57)
ASGR1 Log2(TPM+1)
UCS (n = 57)
ASGR1 Log2(TPM+1)
UCS (n = 57)
UCS (n = 57)
UVM (n = 79)
UVM (n = 79)
UVM (n = 79)
UVM (n = 79)
UVM (n = 79)
UVM (n = 79)
10.0-
P-V.Vo. P=0.73
10.0-
P = 0.40, p = U.U.U.
10.0-
10.0-
p =- v.19, p = 0.16
10.0-
P == U.U.S. p= U.D
ASGR1 Log2(TPM+1)
P =- U.Z, P = 0.13
ASGR1 Log2(TPM+1)
P == 0. 18. p = 0. 12
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
p = U. 18. p= U. IT
ASGR1 Log2(TPM+1)
p =- 0.1. p= 0.50
ASGR1 Log2(TPM+1)
0.0-
PFUUSS. p= UST
P = UNTO. P= UST
ASGR1 Log2(TPM+1)
₹
1
.
p= = 0.10. p = U. 10
UN
7.5
Un
2
P
8
50
V
2
-
S
6
3
4
0.10 0.12 0.14 0.16 0 18 B_cell level
0.12 0.14 0.16 0.18
LA
I
0.17 0.19 021 023 025
0.10 0.11 0.12 0.13
2.00 0.02 0.04
0 50 0 52 0 54 0 56 0.58 Dendritic level
0.0
02
04
0.6
00 01 02 03 04 05 CD4_Tcell level
00
02
0.4
0.6
0.00 0.05 0.10 0.15 0:20
0.0
0.5 1.0 Macrophage level
00
0.5
10
1,5
CD4_Tcell level
CDB_Tcell level
Neutrophil level
Macrophage level
B_cell level
CD8_Tcel level
Neutrophil level
Dendritic level
ASGR1 in human cancers
ACC (n = 77)
ACC (n = 77)
ACC (n = 77)
BLCA (n = 405)
BLCA (n = 405)
BLCA (n = 405)
BRCA (n = 1077
BRCA (n = 1077
BRCA (n = 1077
COAD (n = 282)
COAD (n = 282)
COAD (n = 282)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
pet U.45. D = U.U
ASGR1 Log2(TPM+1)
.0.32, p = 3.18
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
10.29, p = 3.20
ASGR1 Log2(TPM+1)
=0.21, p .= 2.38
ASGR1 Log2(TPM+1)
-0. 4, 03228
ASGR1 Log2(TPM+1)
p = U.38.p < 2.26
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
0 14. P = U.U
ASGR1 Log2(TPM+1)
1000 0 1000 Stromal_score
-1000 0 1000 2000 Immune_score
-2000 0 2000
ESTIMATE_score
-20081000 0 10002000
Stromal_score
-1000 0 100020000000 Immune_score
-2500 0 2500 ESTIMATE_score
-2009-1000 0 1000 2000
-2000 0 20004000
Stromal_score
-1000 0 10002000000 Immune_score
ESTIMATE_score
-2009-1000 0 1000
Stromal_score
-1000 0 1000 2000 3000 Immune_score
-2000 0 2000 4000 ESTIMATE_score
ESCA (n = 181)
ESCA (n = 181)
ESCA (n = 181)
GBM (n = 152)
GBM (n = 152)
GBM (n = 152)
KIRC (n = 528)
KIRC (n = 528)
KIRC (n = 528)
LGG (n = 504)
LGG (n = 504)
LGG (n = 504)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
p =U.A.p =2.10
ASGR1 Log2(TPM+1)
-041,04220
ASGR1 Log2(TPM+1)
P= 043.042.20
ASGR1 Log2(TPM+1)
4.49,P<22
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
-20091000 0 1000 Stromal_score
1000 0 100010008000
40082000 0 20004000
15001000500 0 5001000
-1000 0 1000 2000 Immune_score
-2000 0 2000
-1000 0 1000
Immune_score
ESTIMATE_score
Stromal_score
ESTIMATE_score
Stromal_score
0 10002000 3000 Immune_score
-2500 0 2500 5000 ESTIMATE_score
2000-1000 0 1000
Stromal_score
-1000 0 10002000 Immune_score
-2000 0 2000 ESTIMATE_score
LIHC (n = 363)
LIHC (n = 363)
LIHC (n = 363)
LUAD (n = 500)
LUAD (n = 500)
LUAD (n = 500)
LUSC (n = 491)
LUSC (n = 491)
LUSC (n = 491)
MESO (n = 85)
MESO (n = 85)
MESO (n = 85)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
a-U.13. P= U
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
=U.Z. DF U.UUUU
ASGR1 Log2(TPM+1)
1001
10.0+
= 0.24, p = 7000
ASGR1 Log2(TPM+1)
= 0.25. p=01.70
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
P = U.38.D= U.UUJ
ASGR1 Log2(TPM+1)
O
=1000 0 1000 Stromal_score
-1000 0 100020003000 Immune_score
-2000 0 2000 ESTIMATE_score
2000-1000 0 1000
1000 0 100020003000
-2500 0 2500
-2000-1000 0 1000
-1000 0 100020009000
-2000 0 2000 4000 ESTIMATE_score
0 1000 2000
00 1000000000006000 ESTIMATE_score
Stromal_score
Immune_score
ESTIMATE_score
Stromal_score
Immune_score
Stromal_score
0 1000 2000 3000 Immune_score
OV (n = 417)
OV (n = 417)
OV (n = 417)
PAAD (n = 177)
PAAD (n = 177)
PAAD (n = 177)
PCPG (n = 177)
PCPG (n = 177)
PCPG (n = 177)
PRAD (n = 495)
PRAD (n = 495)
PRAD (n = 495)
ASGR1 Log2(TPM+1)
= 0:22, 0 = 6.30
ASGR1 Log2(TPM+1)
+ U.10, p = U.UUU
ASGR1 Log2(TPM+1)
0.21, p = U.UUU
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
P = U.22 p = 0.UU3
ASGR1 Log2(TPM+1)
3.22, p = U.UUS
ASGR1 Log2(TPM+1)
0.13, p = 0.08
ASGR1 Log2(TPM+1)
0.19. p = U.UT.
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
=Q3, p = 4.88
ASGR1 Log2(TPM+1)
0 - 032, P = 3.68
-0.33, p - 3.48
-2000-1000 0 1000 Stromal_score
20091000 0 10002000
-2000 0 2000
-1000 0 1000 2000 Stromal_score
-1000 0 100020003000
-2000 0 2000 4000 ESTIMATE_score
-2000-1000 0 1000
-1000 0 1000 2000
4000-2000 0 2000 ESTIMATE_score
-2000-1000 0 1000 Stromal_score
-1000 0 100020003000 Immune_score
-2000 0 2000 ESTIMATE_score
Immune_score
ESTIMATE_score
Immune_score
Stromal_score
Immune_score
READ (n = 91)
READ (n = 91)
READ (n = 91)
SARC (n = 258)
SARC (n = 258)
SARC (n = 258)
SKCM (n = 101)
SKCM (n = 101)
SKCM (n = 101)
STAD (n = 388)
STAD (n = 388)
STAD (n = 388)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
P= 0.63, p = 8.30
ASGR1 Log2(TPM+1)
P = UAS, DE 1.50
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
- U.Uo2, p= U.Z.
ASGR1 Log2(TPM+1)
2000-1000 0 1000 Stromal_score
0 1000 2000
-2000 0 2000 ESTIMATE_score
1000 0 1000 2000
-20000000 1000000000
-2000 0 20004000 ESTIMATE_score
20001000 0 10002000 ESTIMATE_score
-2000-1000 0 1000
-1000 0 100020003000
-2500 0 2500 ESTIMATE_score
Stromal_score
Immune_score
-1500-1000-500 0 Stromal_score
1000 0 1000 2000 Immune_score
Immune_score
Stromal_score
Immune_score
TGCT (n = 132)
TGCT (n = 132)
TGCT (n = 132)
THCA (n = 503)
THCA (n = 503)
THCA (n = 503)
THYM (n = 118)
THYM (n = 118)
THYM (n = 118)
UCEC (n = 178)
UCEC (n = 178)
UCEC (n = 178)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
-U5, P = 1.38-
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
FFUZ. P =8.50
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
p = U.Te. p= U.22
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
F U. 13. P = U.UT.
ASGR1 Log2(TPM+1)
=- 0.27, p = 0.0.
2000 -1000 0 1000
1000 0 100020003000
-2000 0 2000 4000 ESTIMATE_score
-2000-1000 00 1000 0 1000 Stromal_score
-1000 0 100020003000
-2000 0 2000 4000 ESTIMATE_score
1500 000500 0 5001000
0 1000 2000 3000 Immune_score
0 2000 4000
2000500000500 0 500
-1000 0 100020003000
ESTIMATE_score
Immune_score
-2000 0 2000 ESTIMATE_score
Stromal_score
Immune_score
Immune_score
Stromal_score
Stromal_score
UCS (n = 56)
UCS (n = 56)
UCS (n = 56)
UVM (n = 79)
UVM (n = 79)
UVM (n = 79)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
ASGR1 Log2(TPM+1)
20
P +45, p _U.UJ
2000-1000 0 1000
-1000 0 1000 2000 Immune_score
30000950000 1000003000
-1500-1000-500
-1000 0 1000 2000
-309/2080 000 0 1000000
Stromal_score
ESTIMATE_score
Stromal_score
Immune_score
ESTIMATE_score
ASGR1 in human cancers
Correlation between ASGR1 expression and immune checkpoint genes expression
VTCN1
TNFSF9
TNFSF4
TNFSF18
TNFSF15
TNFSF14
TNFRSF9
TNFRSF8
TNFRSF4
TNFRSF25
TNFRSF18
TNFRSF14
TMIGD2
TIGIT
p value
PDCD1LG2
PDCD1
NRP1
LGALS9
LAIR1
0
LAG3
KIR3DL1
IDO2
IDO1
ICOSLG
ICOS
HHLA2
HAVCR2
CTLA4
CD86
CD80
CD70
0.8
CD48
CD44
CD40LG
CD40
CD28
CD276
CD274
CD27
CD244
CD200R1
-0.6
CD200
CD160
BTNL2
BTLA
ADORA2A
ACC (n = 77)
BLCA (n = 407)
BRCA (n = 1092)
CHOL (n = 36)
COAD (n = 288)
DLBC (n = 47)
ESCA (n = 181)
GBM (n = 153)
HNSCC (n = 518)
KICH (n = 66)
KIRC (n = 530)
KIRP (n = 288)
LAML (n = 173)
LGG (n = 509)
LIHC (n = 369)
LUAD (n = 513)
LUSC (n = 498)
MESO (n = 87)
OV (n = 419)
PAAD (n = 178)
PCPG (n = 177)
PRAD (n = 495)
READ (n = 92)
SARC (n = 258)
SKCM (n = 102)
STAD (n = 414)
TGCT (n = 148)
THCA (n = 504)
THYM (n = 119)
UCEC (n = 180)
UCS (n = 57)
UVM (n = 79)
ASGR1 in human cancers
| KEGG signaling pathways | Observed in cancer | Observed in cancer count | Enrichment score | P value | Leading genes of the signaling pathway |
|---|---|---|---|---|---|
| Olfactory transduction | ACC | 14 | 0.869 | 0.011 | GNAL |
| Olfactory transduction | ACC | 14 | 0.869 | 0.011 | GNAL |
| Olfactory transduction | CHOL | 14 | -0.935 | 0.002 | PRKACB, GNAL, OR2A7, PRKG1 |
| Olfactory transduction | ESCA | 14 | -0.961 | 0.006 | OR2A7, CLCA2, CALML3, CLCA4, CALML5 |
| Olfactory transduction | GBM | 14 | -0.903 | 0.002 | CAMK2G, CNGA3, PRKG1 |
| Olfactory transduction | KIRC | 14 | 0.912 | 0.006 | OR2A4 |
| Olfactory transduction | KIRP | 14 | -0.778 | 0.023 | PRKX, GNAL, OR2A7, CAMK2B |
| Olfactory transduction | LAML | 14 | 0.919 | 0.002 | GNAL, CAMK2D |
| Olfactory transduction | LIHC | 14 | -0.952 | 0.001 | ADCY3, PRKX |
| Olfactory transduction | PAAD | 14 | 0.886 | 0.04 | CAMK2B, GNAL |
| Olfactory transduction | PCPG | 14 | 0.896 | 0.002 | PRKG1, PRKX |
| Olfactory transduction | SARC | 14 | -0.885 | 0.003 | PRKACB, CAMK2D, CAMK2G, PRKG1, GNAL, PDE1C |
| Olfactory transduction | SKCM | 14 | -0.807 | 0.043 | GNAL, PRKG2 |
| Olfactory transduction | TGCT | 14 | -0.942 | 0.004 | GUCA1A, GNAL, OR2B6, OR7E24 |
| Cytokine cytokine receptor interaction | BLCA | 11 | 0.935 | 0.002 | CCL21, CXCR6, OSM, IL2RA, TNFRSF19, TNFRSF11B, CCL8, LIFR, CXCL3, TGFB2, IL12RB1, CCR5, TN- FRSF10D, TNFSF9, CCL3L3, NGFR, IL24, XCL1, IL18R1, AMH, KIT, CCL14, CCL28, CCL11, TNFRSF10C, IL6, CSF2RB, TNFRSF6B, IL11, CCR1, CCL19, CCL13, IL7R, TNFSF13B, CXCL2, CXCL11, CCL4L2, IL2RB, IL10RA, CXCL13, CD27, CXCL9, FLT4, CCL18, IL1R2, CXCL12, TNFRSF4, PDGFC, CCL3, CCL4, IL17RB, PDGFRA, CSF3R, CSF1R, TNFRSF18, IL15, INHBA, LIF, TGFB3, CCR7, IL6R, CXCL10, CCL20, CCL2, ACVRL1, LTB, TNFRSF1B, IL11RA, CSF1, CCL5, IL2RG, RELT, PDGFRB, ACVR2B, PLEKHO2, CLCF1 |
| Cytokine cytokine receptor interaction | BRCA | 11 | 0.874 | 0.042 | IL6, IL12RB1, CXCL2, OSM, CCL17, CCL13, CNTFR, TNF, IL2RA, XCL2, TNFRSF6B, CCL11, TNFRSF17, IL18R1, IL21R, CCR2, CXCR3, CXCR6, CXCR5, LIF, CCR7, CCL18, NGFR, CXCL8, CCL21, BMP7, CCL22, LTB, CCL8, TNFRSF4, CD27, CSF2RB, IFNLR1, TNFRSF11A, CCL19, CCR5, EGFR, TNFSF8, IL7R, IL15, TNFRSF25, CCL3L3, RELT, CXCL13, IL2RB, MET, CXCL11, KIT, IL24, TNFRSF10C, IL6R, IL10RA, CX3CL1, FLT4, FLT3LG, IL2RG, IL1B, TNFRSF1B, CCL5, IL15RA, IL11RA, CCL4, CXCL9, CD40, CCL4L2, CCR1, TNFRSF18, CCL14, CCL3, ACVRL1, CTF1, TNFSF13B |
| Cytokine cytokine receptor interaction | CHOL | 11 | -0.862 | 0.037 | ACVRL1, CCL21, PDGFRA, TNFRSF19, RELT, CCL28, OSMR, FLT3LG, IL2RG, TNFRSF1B, CXCL3, CSF3R, CSF1, IL15, CCL3, CSF1R, VEGFC, CCL5, TNFSF13B, IL23A, TNFRSF4, KITLG, CCL4, CXCL10, CCL4L2, IL10RA, HGF, CCL18, CXCL9, TNFRSF18, CCL3L3, CXCL14, IL7, CCL19, IL1B, CCL22, OSM, IL21R, TNFSF4, TNFSF11, IL6, IL20RB, TNFSF8, CCR2, XCL2, IL18R1, LIFR, CCL17, TNFSF9, IL12RB1, CCR7, CCR1, CXCR3, CCR5, CXCR6, CXCL13, CXCL11, CSF2RB, TNFRSF6B, IL2RB, IL7R, CD27 |
| Cytokine cytokine receptor interaction | KICH | 11 | 0.931 | 0.002 | CSF3R, CCL18, LTB, CXCL9, CCL4L2, IL1B, CCR1, CX3CR1, TNFRSF10A, TNFRSF19, OSM, HGF, TNFSF13B, EDA2R, CCL8, CXCL8, CSF2RB, IL1RAP, LEPR, IL6, IL17RB, IL10RA, IL2RG, TNFSF15, IL2RB, BMP2, CCL4, CCL3, TNFRSF10D, IL1R1, INHBB, IL4R, CXCL12, TNFRSF4, FAS, GHR, OSMR, TNFRSF1B, CSF1R, IL6R, RELT, IFNAR2, BMPR1A, CCL21, PLEKHO2, PDGFA, CCL2, ACVR2A, CXCL10, TGFB3, CCL14, CCL5, FLT4, PDGFB |
| Cytokine cytokine receptor interaction | KIRP | 11 | 0.952 | 0.001 | CXCL9, IL2RB, CCL20, CXCR6, CCR2, TNFRSF4, OSM, CSF2RB, IL1R2, PF4V1, IL12RB1, HGF, CCL21, CCR7, TNFRSF18, TNFSF8, CXCR3, CCL17, CXCL5, CXCL3, KIT, CCR5, CCL3L3, IL7R, TNFSF13B, CXCL10, IL1B, CD27, CCR1, RELT, TNFRSF6B, IL12RB2, IL15RA, CCL4L2, CCL18, CXCL2, CSF3R, CD70, IL10RA, CCL3, VEGFC, CCL4, CXCL8, CX3CR1, IL2RG, CXCL6, LTB, KDR, CCL5, TNFRSF1B, ACVRL1, IL1RAP, INHBB, CCL15, CXCR5, CCL14, CXCL1, CSF1R, IL18R1, CXCR4, PDGFRB, IL15, CSF1, PDGFB, TGFB3, CXCL12 |
ASGR1 in human cancers
| Cytokine cytokine receptor interaction | LGG | 11 | -0.919 | 0.003 | PDGFB, IL17RA, BMPR1B, PDGFA, TNFSF13, TNFRSF1B, IL1RAP, TNFRSF1A, TGFBR2, CXCR4, NGFR, TNFRSF14, CX3CR1, CSF3R, IL13RA1, LTBR, IL15RA, CCL2, IL18, TNFRSF19, IL10RA, TNFRSF12A, CXCL14, IL1B, IL6R, TNFSF13B, FAS, TNFSF10, LEPR, CD40, CCR1, OSMR, TGFB2, IL1R1, EDA, EDA2R, IL2RG, CTF1, GHR, CNTF, CCL5, HGF, CXCL3, EGF, TNFRSF10D, LTB, CXCL10, TNFSF8, TNF, TNFRSF10C, IL12RB1, CCL19 |
| Cytokine cytokine receptor interaction | LUAD | 11 | 0.907 | 0.049 | CSF2, TNFRSF9, CCR6, CX3CR1, CCR4, BMP7, TNFSF14, TNF, CCL23, EGF, CCL17, XCL2, OSM, IL24, TNFSF9, IFNLR1, CCL3L3, IL21R, IL23A, IL12RB1, CCL8, CCR2, CCL22, CXCR3, TNFRSF4, IL7, TNFRSF18, CCR7, IL18R1, CCL13, CCR5, RELT, IL2RA, FLT4, CSF2RB, CCL28, CCR1, EPOR, IL11RA, IL17RB, TNFRSF25, CCL3, LTB, TNFSF4, CXCL3, CSF3R, TNFSF8, ACVRL1, FLT3LG, IL20RB, CCL18, CSF1R, CX3CL1, CXCR6, CCL4L2, IL1B, CD40, IL2RB, TNFSF12, TNFRSF14, CXCL11, CXCL14, IL10RA, TNFSF13, CCL19, IL17RA, CCL14, PLEKHO2 |
| Cytokine cytokine receptor interaction | OV | 11 | 0.903 | 0.037 | CCL18, OSM, TNFSF4, IL23A, CCL21, IL10, CXCR3, IL7R, CSF2RB, TNFRSF10D, TNFSF8, TNFRSF10C, CCL14, CNTFR, KITLG, IL6, CXCL13, INHBA, EDA, TNFRSF6B, FLT4, PDGFRA, CD27, CXCL3, TNFRSF18, KDR, CCL8, LEPR, RELT, IL10RA, IL2RB, TNFRSF4, TGFB3, CSF3R, FLT1, TNF, CCR1, IFNLR1, CXCL14, TNFRSF19, ACVRL1, IL17RB, CXCL9, IL1B, PDGFRB, CXCL12, ACVR2B, TGFBR1, TNFRSF1B, LTB, IL4R, VEGFC, EGFR, CXCL2, CSF1R, CCL3 |
| Cytokine cytokine receptor interaction | PRAD | 11 | 0.929 | 0.004 | CCL3L3, LIF, CXCL13, CXCR6, CX3CR1, IL1B, CXCL6, CCR5, CNTFR, CSF2RB, RELT, CCR1, TNFRSF10D, CCR7, CCL20, TNFRSF4, CCL23, TNFSF13B, IL7, KIT, CSF3R, TNFRSF18, INHBA, IL6, CXCR5, IL2RB, TNFRSF25, IL15, FLT4, CXCL2, CD27, CXCL1, LTB, CCL3, BMP7, IL18, IL10RA, CCL4L2, HGF, NGFR, CTF1, MET, CCL21, FLT3LG, ACVRL1, TNFRSF1B, TNFRSF11A, CCL4, PDGFRA, EPOR, CCL19, IL7R, CD40, CSF1R, VEGFC, CXCL8, CXCL14, CSF1, CCL18, TGFB1, CCL5, CCL2, CX3CL1, PLEKHO2, IL15RA, CLCF1 |
| Cytokine cytokine receptor interaction | SKCM | 11 | 0.922 | 0.05 | TNFRSF18, CXCR3, IL12RB1, CD70, CXCL11, IL1R2, CXCR5,CCL8, CCR5, BMP7,CCL3L3, CXCR6, CSF2RB, CCR7, IL15, CCL20, KITLG, INHBA, IL7R, INHBB, TNFSF4, TNFRSF10C, TNFRSF10A, FLT4, EGFR, IL21R, TNFRSF25, PDGFC, CXCL13, LIF, IL20RB, IL1B, CCL4L2, IL2RB, CD27, CLCF1, TNFSF13B, VEGFC, CCL13, IL15RA, CXCL9, CCR1, IL18, LTB, CXCL10, CCL21, CSF3R, TNFRSF4, IL10RA, CCL19, CCL4, FLT3LG, OSMR, TNFSF10, TNFRSF6B, IL2RG, NGFR, CSF1R, IFNLR1, PDGFRA, FLT1, CD40, IL1R1, TNFRSF1B, CCL5, PDGFA, FAS, ACVR2A, CXCL16, TGFB3, CSF1, CCL17, TNFSF9, CCL3, CXCR4, CXCL14, CCL2, TGFB2, ACVRL1, PDGFB, IL6 |
| Cytokine cytokine receptor interaction | UCEC | 11 | 0.892 | 0.04 | AMH, CCL19, TNFSF9, BMP2, CCR5, CXCR3, INHBA, IL7R, TNFRSF11B, OSM, CSF2RB, FLT4, CXCR6, CCL8, CCL3L3, CCL21, CXCL13, TNFRSF6B, CXCL11, CCL14, IL6, PDGFRA, TNFSF13B, CXCL9, BMP7, CD27, CCR1, IL2RB, CCL18, TNFRSF10D, IL10RA, TNFRSF10C, TNFRSF4, IL23A, CXCL12, CD40, TNF, CSF3R, IL15RA, CSF1R, FLT3LG, RELT, PDGFA, CCL3, CCL4, CCL5, CXCR5, TGFB1, PDGFB, LIF, IL1B, TNFRSF18, LTB, IL2RG, CTF1, IL17RA, TNFRSF1B, ACVRL1, LEPR, IL11RA |
| Chemokine signaling pathway | BLCA | 8 | 0.94 | 0.004 | CCL21, CXCR6, GNG4, CCL8, CXCL3, PIK3R5,GNB3,CCR5,CCL3L3, XCL1, CCL14, GNGT2, CCL28, CCL11, DOCK2, CCR1, CCL19,CCL13,CXCL2, CXCL11,CCL4L2, RASGRP2,GNG2,NCF1, CXCL13, CXCL9, VAV1, CCL18, PIK3CD, HCK, CXCL12, WAS, GNB4, CCL3, CCL4, AKT3, JAK3, PLCB2, CCR7, ELMO1, ADCY7, PREX1, CXCL10, ADCY4, CCL20, CCL2, ADCY9, SHC2, GNG11, CCL5, ARRB1, GRK5, JAK2, FGR, CX3CL1 |
| Chemokine signaling pathway | BRCA | 8 | 0.916 | 0.018 | CXCL2, CCL17, CCL13, ITK, PRKCB, GNG4, XCL2, CCL11, PIK3CG, CCR2,CXCR3, CXCR6, CXCR5, CCR7, CCL18, CXCL8, CCL21, JAK3, RASGRP2,CCL22, CCL8, GNGT2, CCL19, PIK3CD, CCR5, NCF1, PIK3R5, FGR, CCL3L3, CXCL13, WAS, GNG7, CXCL11, PLCB2, ADCY2, VAV1, ADCY4, CX3CL1, DOCK2, CCL5, CCL4, ADCY7, CXCL9, CCL4L2, HCK, CCR1, CCL14, CCL3, PRKX, GRK5, LYN, CXCL10, RAC2, NFKBIB, CCL2, SHC2 |
| Chemokine signaling pathway | CHOL | 8 | -0.881 | 0.048 | CCL21, PREX1, CCL28, CXCL3, PLCB2, AKT3,CCL3, CCL5, JAK2, GNB4, ADCY7, GNG2, JAK3, WAS, HCK, VAV1, CCL4, CXCL10, NCF1, FGR, CCL4L2, PIK3CD, CCL18, CXCL9, DOCK2, CCL3L3, CXCL14, CCL19, PIK3R5, PLCB4, CCL22, PRKCB, ITK, CCR2, XCL2, CCL17, CCR7, CCR1, CXCR3, CCR5, CXCR6, CXCL13, CXCL11, GNGT2 |
| Chemokine signaling pathway | KICH | 8 | 0.928 | 0.017 | CCL18, CXCL9, FGR, CCL4L2, NCF1, ADCY7, DOCK2, CCR1, CX3CR1, RASGRP2, GRK4, VAV1, GNAI3, GNGT2, PIK3R5, CCL8, CXCL8, JAK3, PIK3CD, WAS, PLCB2, GNB4, GNG2, CCL4, ADCY4, CCL3, RAC2, ARRB1, LYN, CXCL12, GRK5, ELMO1, PRKX, PIK3R1, CCL21, HCK, CCL2, CXCL10, CCL14, CCL5, PREX1, CXCR4 |
ASGR1 in human cancers
| Chemokine signaling pathway | KIRP | 8 0.947 | 0.002 | CXCL9, CCL20, CXCR6, CCR2, PF4V1, TIAM1,CCL21, CCR7, PRKCB, CXCR3, CCL17, CXCL5, CXCL3, CCR5, ADCY4, CCL3L3, CXCL10, PIK3R5, GNGT2, CCR1, VAV1, RASGRP2, DOCK2, GNG2, CCL4L2, CCL18, CXCL2, NCF1, FGR, GNB4, HCK, CCL3,CCL4, CXCL8, CX3CR1, CXCL6, PREX1, ELMO1, WAS, JAK3, GRK5, CCL5, PLCB2, CCL15, CXCR5, CCL14, ADCY3, CXCL1, ADCY7, RAC2, CXCR4 |
| Chemokine signaling pathway | LAML | 8 0.899 | 0.017 | CXCR5, GNB3, TIAM1, CCR5, GNGT2, CXCL16,CCL3L3,CXCR3, CCR2, CCR1, PTK2, CX3CR1, GRK5, NCF1, PF4, CXCR2, CCL3, ADCY9, PRKCZ, AKT3, CXCL3, FGR, VAV2, HCK, CCR7, CCL23, PIK3R5, ADCY6, ADCY7, CXCL12, PRKACA, ARRB2, MAP2K1, PAK1, GNAI1, PRKCD, ITK, PPBP, CXCR6, CXCL2, CXCL8, PLCB3, PREX1, JAK2, PXN, IKBKG, CCL4, ADCY4, NFKBIB, PIK3CD, GNB5, NFKBIA, WAS, MAPK3, CXCR4, LYN, PLCB1, PRKCB, GNG10, HRAS, CSK, CRK, GRB2, STAT2, VAV1, GNB2, GRK6, GNAI2, PTK2B, NFKB1, GNAI3, RAC1, CCL5, PLCB2, GSK3A, GNG2 |
| Chemokine signaling pathway | PRAD | 8 0.932 | 0.01 | JAK3, CCL3L3, GNGT2, CXCL13, CXCR6, CX3CR1, ADCY7, CXCL6, CCR5, PIK3R5, CCR1, CCR7, CCL20, CCL23, PRKCB, VAV3, CXCR5, VAV1, PIK3CD, NCF1, CXCL2, DOCK2, CXCL1, FGR, CCL3, HCK, WAS, CCL4L2, TIAM1, ADCY4, PLCB2, RASGRP2,CCL21, GNG2,CCL4, GNG11, GNB4, GRK5, CCL19, ADCY5, RAC2, CXCL8, CXCL14, CCL18, ADCY3, CCL5, CCL2, ELMO1, CX3CL1 |
| Chemokine signaling pathway | SKCM | 8 0.936 | 0.031 | CXCR3, CXCL11, CXCR5, PIK3R5, CCL8,CCR5, CCL3L3, CXCR6, GNGT2, CCR7, CCL20, PLCB1, ITK, PRKCB, CXCL13, VAV1, RASGRP2, FGR, CCL4L2, DOCK2, JAK3, ADCY4, CCL13, NCF1, CXCL9, CCR1, JAK2, CXCL10, CCL21, CCL19, CCL4, ELMO1, WAS, VAV3, ADCY7, HCK, RAC2, PLCB2, CCL5, TIAM1, CXCL16, ADCY2, LYN, CCL17, CCL3, CXCR4, CXCL14, CCL2 |
| Neuroactive ligand receptor interaction | HNSCC | 8 0.926 | 0.02 | P2RX5, GABRP, PTGIR, CTSG, GPR35, ADORA3, EDNRB, CHRNA1, ADRA2C, ADRA2A, F2RL3, P2RY10, P2RX7, ADORA2A, S1PR4, THRB, FPR1, APLNR, C3AR1, S1PR3, PTGER4, F2RL2, FPR3, C5AR1, MC1R, S1PR1 |
| Neuroactive ligand receptor interaction | KIRP | 8 0.937 | 0.001 | P2RY13, S1PR4, PTGER4, ADRB2, CTSG, OPRL1, P2RX7, S1PR2, FPR1, ADORA3, FPR3, ADORA2A, S1PR3, GZMA, GPR35, PTAFR, C3AR1, P2RY6, NPY1R, S1PR1, C5AR1 |
| Neuroactive ligand receptor interaction | LAML | 8 0.91 | 0.004 | S1PR3, GABRR2, NMUR1, P2RY6, GRIN2C, ADORA3, S1PR5, LPAR1, RXFP1, HTR1F, TACR2, FPR2, FPR1, VIPR1, PTGIR, C5AR1, PTH2R, F2RL2, CALCRL, GABBR1, P2RY13, F2RL1, PTAFR, GPR35, CHRNE, HRH2, P2RX7, ADORA2A, ADORA2B |
| Neuroactive ligand receptor interaction | LUAD | 8 0.914 | 0.029 | TBXA2R, LTB4R2, FPR2, PRSS2, P2RX1, OPRL1, ADRB2, P2RY14, P2RX7, PTH1R, P2RY13, GIPR, ADO- RA1, S1PR4, SCTR, GABRE, PTGIR, LTB4R, P2RX5, CYSLTR1, GABBR1, MC1R, APLNR, P2RY10, P2RY11, FPR1, GPR35, PTGER4, ADORA3, C5AR1, ADORA2A, PTAFR, C3AR1, F2RL3, FPR3, VIPR1, PTGER2 |
| Neuroactive ligand receptor interaction | OV | 8 0.918 | 0.006 | GRIN2D, ADORA2A, PTGIR, EDNRB, P2RX7, CHRNA5, PTGER3, ADRA2A, PTGER4, APLNR, FPR1, DRD4, EDNRA, TACR2, LEPR, LPAR1, ADORA3, LTB4R, S1PR1, GLRB, FPR3, GABBR1, S1PR3, GRIK5, MC1R, F2R, GPR35, GZMA, S1PR2, C5AR1, PTGER1, PTH2R, CALCRL, C3AR1 |
| Neuroactive ligand receptor interaction | PAAD | 8 0.915 | 0.018 | CTSG, AGTR1, GHR, P2RY13, OXTR, OPRL1, LTB4R2, GRIA3, S1PR5, CYSLTR1, P2RX7, TBXA2R, GRIK5, GABRD, PTGER3, PTH1R, P2RX5, S1PR4, GRIN2D, GRID1, ADORA2A, P2RY1, P2RY6, ADORA3, C5AR1, ADRA2C, FPR3, FPR1, S1PR2, LTB4R, MC1R, F2RL3, C3AR1 |
| Neuroactive ligand receptor interaction | STAD | 8 0.917 | 0.045 | PRSS1, HTR1D, PRLR, GABRD, ADRA2C, PTGER1, PRSS2, ADORA3, CHRNA7, P2RX1, P2RY2, CHRM3, LTB4R2, GABRE, TBXA2R, P2RY6, P2RX5, MC1R, P2RY13, PTGER3, P2RX7, FPR1, BDKRB1, PTGIR, ADORA2A, CHRNB1, S1PR4, F2RL3, LTB4R |
| Neuroactive ligand receptor interaction | UVM | 8 0.944 | 0.001 | HTR2B, PTGER4, LHB, C3AR1, CHRNE, FPR3, GZMA, ADRA2A, S1PR1, TBXA2R, GABRB3, PTH1R, P2RY6, GRID1, HRH2, OPRL1, P2RX6, ADORA2A |
| Hematopoietic cell lineage | BLCA | 5 0.946 | 0.04 | ITGAM, CD8B, CD33, IL2RA, CD38, ITGA4, CD5, KIT, IL6, IL11, CD8A, IL7R, ANPEP, IL1R2, CD2, FCGR1A, CSF3R, CSF1R, CD7, CD37, IL6R, CD3E, CD36, MME, CD3D, CD4, ITGA1, IL11RA, CD14, CSF1, HLA- DRB5 |
| Hematopoietic cell lineage | KICH | 5 0.944 | 0.015 | CSF3R, CD3E, IL1B, MME, ITGAM, CD3D, ANPEP, CD33, CD2, ITGA4, CD8A, IL6, CD7, FCGR1A, IL1R1, IL4R, CD4, CD37, CSF1R, IL6R, ITGA2, HLA-DRB5 |
| Hematopoietic cell lineage | KIRP | 5 0.954 | 0.014 | IL1R2, CD8B, ITGA4, CD5, CD1C, CD1D, KIT, IL7R, IL1B, CD8A, CD7, CD3D, CD2, CD33, CD3E, CD36, ITGAM, CSF3R, FCGR1A, CD4, CD37, CD22, CSF1R, MME, ITGA5, CD14, HLA-DRB5, CD44, CSF1 |
ASGR1 in human cancers
| Hematopoietic cell lineage | PCPG 5 | 0.944 | 0.018 | GP1BB, IL6, CD1D, CD7, ITGB3, CD3D, ANPEP, ITGAM, CD33, IL6R, CD3E, CD36, FCGR1A, CSF3R, KITLG, IL4R, IL1R1, ITGA5 |
| Hematopoietic cell lineage | UCEC 5 | 0.935 | 0.046 | ITGAM, IL7R, MME, ITGB3, ITGA2, CD36, CD8B, CD33, CD38, CD8A, IL6, CD7, ITGA2B, CD2, TNF, CD3E, CSF3R, CD3D, CSF1R, FLT3LG, FCGR1A, IL1B, ITGA5, CD14, CD4, IL11RA |
| Complement and coagulation cascades | ACC 4 | -0.937 | 0.022 | SERPINA5, A2M, CFD, CD59, TFPI, C1S, C1QB, SERPING1, C1QA, C1QC, F8, PROS1, C7, C3, F3, CFH, PLAT, C2, THBD, C3AR1, SERPINA1, PLAU, CFB, FGG, F13A1 |
| Complement and coagulation cascades | ACC 4 | -0.937 | 0.022 | SERPINA5, A2M, CFD, CD59, TFPI, C1S, C1QB, SERPING1, C1QA, C1QC, F8, PROS1, C7, C3, F3, CFH, PLAT, C2, THBD, C3AR1, SERPINA1, PLAU, CFB, FGG, F13A1 |
| Complement and coagulation cascades | LAML 4 | 0.942 | 0.022 | F5, THBD, SERPINF2, C3, F12, F3, C1QA, C1QC, C2, C5AR1, CR1, C1QB, SERPINA1, VWF, SERPIND1 |
| Complement and coagulation cascades | TGCT 4 | 0.928 | 0.042 | PROC, FGA, C5, FGB, FGG, F5, F2, MASP1, SERPINF2, F10, F13A1, THBD, PLAT, TFPI, F3, CFI, SERPINE1, SERPINA1, C5AR1, CFB, CFH, F12 |
| Drug metabolism cytochrome p450 | ACC 4 | -0.916 | 0.039 | MGST1, GSTP1, GSTA3, ADH1B, MAOA, MAOB, CYP3A5, ALDH3B1, FMO5, GSTA1, FMO4, UGT1A7, CYP3A4, GSTA2 |
| Drug metabolism cytochrome p450 | ACC 4 | -0.916 | 0.039 | MGST1, GSTP1, GSTA3, ADH1B, MAOA, MAOB, CYP3A5, ALDH3B1, FMO5, GSTA1, FMO4, UGT1A7, CYP3A4, GSTA2 |
| Drug metabolism cytochrome p450 | DLBC 4 | 0.945 | 0.037 | GSTM1, ALDH1A3, AOX1, FMO1, MGST1, MAOB |
| Drug metabolism cytochrome p450 | GBM 4 | -0.97 | 0.008 | MAOA, MGST1, FMO5, FMO4, AOX1, GSTO2, ALDH3A1, CYP3A5 |
| Natural killer cell mediated cytotoxicity | BRCA 4 | 0.911 | 0.043 | KLRD1, TNF, PRKCB, SH2D1A, PRKCA, PIK3CG, ZAP70, PRF1, GZMB, KLRK1, CD247, LCK, FCGR3B, PIK3CD, LAT, PIK3R5, TNFRSF10C, VAV1, PLCG2, HCST, CD48, ICAM2, ITGB2, ICAM1, RAC2, FYN, SHC2, LCP2, PTK2B, ITGAL, FAS, SYK, TYROBP |
| Natural killer cell mediated cytotoxicity | CHOL 4 | -0.911 | 0.046 | IFNGR1, NFATC2, SYK, PPP3CC, HLA-E, TNFSF10, HLA-B, PPP3CA, ICAM2, PIK3CA, FAS, TYROBP, RAC2, TNFRSF10A, NFATC1, LAT, PTK2B, FCER1G, ITGB2, LCP2, FCGR3A, HCST, ITGAL, VAV1, CD48, MICB, PIK3CD, ZAP70, GZMB, CD247, LCK, PRF1, PIK3R5, PRKCB, FCGR3B, KLRD1, KLRK1 |
| Natural killer cell mediated cytotoxicity | GBM 4 | 0.93 | 0.035 | ZAP70, KLRK1, ULBP3, GZMB, PIK3CG, PRF1, TNFRSF10D, KLRC2, VAV3, CD247, TNFRSF10C |
| Natural killer cell mediated cytotoxicity | LAML 4 | 0.887 | 0.048 | FCGR3B, NCR1, TNF, PRKCA, TNFRSF10C, FCGR3A, VAV2, TNFSF10, GZMB, FCER1G, CD48, ICAM1, NCR3, PIK3R5, KLRD1, LCK, KLRK1, ITGB2, ITGAL, CD247, MAP2K1, PAK1, TYROBP, SH3BP2, FYN, IFNGR2, TNFRSF10D, IFNGR1, TNFRSF10B, TNFRSF10A, HCST, PLCG1, SYK, MICA, PIK3CD, NFATC1, MICB, HLA-B, MAPK3, HLA-E, PRKCB, LAT, HRAS, GRB2, PTPN6, BID, VAV1, HLA-C, ICAM2, PTK2B, RAC1, FAS, HLA-A, CHP1 |
| Retinol metabolism | BRCA 4 | 0.953 | 0.035 | CYP26B1, DHRS9, ADH1B, RDH5 |
| Retinol metabolism | GBM 4 | -0.966 | 0.014 | DGAT2, ALDH1A1, RPE65, BCO1, CYP26B1, LRAT, CYP3A5 |
| Retinol metabolism | KIRP 4 | 0.949 | 0.048 | DHRS9, DGAT2, UGT1A9, CYP2C9 |
| Retinol metabolism | PAAD 4 | -0.938 | 0.028 | UGT2B15, DHRS9, ALDH1A2, UGT1A10, UGT2A3, CYP2C18, CYP2C9, UGT1A6, ADH6, UGT2B7, BCO1, RDH12 |
| Cardiac muscle contraction | HNSCC 3 | 0.951 | 0.031 | ACTC1, MYL2, CACNA2D4, FXYD2, TNNI3, CACNA2D3, CACNA2D1, TNNC1, CACNB1, COX4I2 |
| Cardiac muscle contraction | STAD 3 | 0.964 | 0.018 | FXYD2, TNNI3, ATP1B2, CACNB2, ATP1A3, CACNA1D |
| Cardiac muscle contraction | UCEC 3 | 0.938 | 0.048 | CACNG4, CACNA2D2, CACNA2D4, FXYD2, COX4I2, TNNT2, UQCRHL |
| Cell adhesion molecules cams | BLCA 3 | 0.939 | 0.033 | ITGAM, CD6, CD8B, SIGLEC1, SPN, NRCAM, ITGA9, SELE, ITGA4, CTLA4, MPZ, PDCD1LG2, HLA-DOB, JAM2, SELP, CLDN11, CD274, ITGAL, CD8A, HLA-DQA2, ICOSLG, CD86, CNTNAP1, PTPRC, HLA-DOA, VCAM1, CD2, SELPLG, SELL, JAM3, CADM1, CLDN3, ITGB2, HLA-DQA1, CD4, CLDN5, VCAN, ICAM1, HLA-DMB, HLA-DRB5, NLGN2, SDC3, HLA-DPB1, HLA-DPA1, SDC2, CDH5, ITGB7, HLA-DQB1, HLA-DRB1, PECAM1 |
| Cell adhesion molecules cams | KIRP 3 | 0.945 | 0.006 | SIGLEC1, CD6, CADM3, HLA-DOB, CD8B, ITGA4, CLDN14, NFASC, SELP, SELL, L1CAM, CD8A, JAM2, SPN, CD2, HLA-DQA2, HLA-G, ITGAL, CD86, ITGAM, PTPRC, HLA-DOA, SELPLG, CD274, CD4, CNTNAP1, CD22, CLDN5, ITGB2, HLA-DQB1, HLA-DQA1, PECAM1, HLA-DRB5, CDH5, JAM3, ICAM1, CD34, HLA-DMA, HLA- DPB1, HLA-DRB1, HLA-DPA1, HLA-DMB, HLA-DRA, ICAM3, SDC3 |
ASGR1 in human cancers
| Cell adhesion molecules cams | LAML | 3 0.921 | 0.011 | CD276, HLA-DQA2, SIGLEC1, SDC3, CLDN7, SDC1, PTPRM, CD28, CD40LG, VCAN, CD40, CD86, SDC4, CD8B, ESAM, HLA-DQB1, ITGAM, HLA-DOA, HLA-DMB, HLA-DRB5, HLA-DOB, CNTNAP1, HLA-DQA1, CD4, ICAM1, NEGR1, HLA-DRB1, HLA-F, CD8A, MPZ, JAM3, ITGB7, HLA-DPA1, HLA-DPB1, CD2, CD22, NCAM1, PECAM1, ITGB2, HLA-DRA, NLGN2, ITGAL, HLA-DMA, CD6, ICOSLG |
|---|---|---|---|---|
| Metabolism of xenobiotics by cytochrome p450 | ACC | 3 -0.929 | 0.035 | ADH1B, AKR1C3, AKR1C1, CYP3A5, AKR1C2, ALDH3B1, GSTA1, UGT1A7, CYP3A4, CYP1B1, GSTA2 |
| Metabolism of xenobiotics by cytochrome p450 | ACC | 3 -0.929 | 0.035 | ADH1B, AKR1C3, AKR1C1, CYP3A5, AKR1C2, ALDH3B1, GSTA1, UGT1A7, CYP3A4, CYP1B1, GSTA2 |
| Metabolism of xenobiotics by cytochrome p450 | MESO | 3 -0.943 | 0.05 | CYP2E1, ADH1C, ADH1B, AKR1C2, CYP3A5, ALDH3A1, GSTM1 |
| Systemic lupus erythematosus | ACC | 3 -0.95 | 0.026 | C1S, C1QB, FCGR3A, C1QA, HLA-DRA, C1QC, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DQB1, HLA-DRB1, FCGR1A, CD40, C7, C3, FCGR2A, C2, HLA-DQA1, HLA-DRB5, CD86, FCGR2B, HLA-DOA |
| Systemic lupus erythematosus | ACC | 3 -0.95 | 0.026 | C1S, C1QB, FCGR3A, C1QA, HLA-DRA, C1QC, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DQB1, HLA-DRB1, FCGR1A, CD40, C7, C3, FCGR2A, C2, HLA-DQA1, HLA-DRB5, CD86, FCGR2B, HLA-DOA |
| Systemic lupus erythematosus | LAML | 3 0.946 | 0.036 | C3, HLA-DQA2, FCGR3B, TNF, C1QA, C1QC, CD28, C2,CD40LG, C1QB, FCGR2B, FCGR2C, CD40, CD86, HLA-DQB1, FCGR3A, HLA-DOA, HLA-DMB, HLA-DRB5, FCGR2A, HLA-DOB, HLA-DQA1, HLA-DRB1, HLA- DPA1, HLA-DPB1, FCGR1A, HLA-DRA, HLA-DMA, C4B, C1R, C4A |
| Allograft rejection | ACC | 2 -0.968 | 0.041 | HLA-DMB, HLA-A, HLA-C, HLA-E, HLA-B, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-F, HLA-DQB1, HLA-DRB1, CD40, HLA-DQA1, HLA-DRB5, CD86, HLA-DOA |
| Allograft rejection | ACC | 2 -0.968 | 0.041 | HLA-DMB, HLA-A, HLA-C, HLA-E, HLA-B, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-F, HLA-DQB1, HLA-DRB1, CD40, HLA-DQA1, HLA-DRB5, CD86, HLA-DOA |
| Autoimmune thyroid disease | ACC | 2 -0.968 | 0.013 | HLA-DMB, HLA-A, HLA-C, HLA-E, HLA-B, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-F, HLA-DQB1, HLA-DRB1, CD40, HLA-DQA1, HLA-DRB5, CD86, HLA-DOA |
| Autoimmune thyroid disease | ACC | 2 -0.968 | 0.013 | HLA-DMB, HLA-A, HLA-C, HLA-E, HLA-B, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-F, HLA-DQB1, HLA-DRB1, CD40, HLA-DQA1, HLA-DRB5, CD86, HLA-DOA |
| Drug metabolism other enzymes | ACC | 2 -0.944 | 0.034 | CYP3A5, DPYD, NAT1, UGT1A7, CYP3A4, CES1, DPYS |
| Drug metabolism other enzymes | ACC | 2 -0.944 | 0.034 | CYP3A5, DPYD, NAT1, UGT1A7, CYP3A4, CES1, DPYS |
| Graft versus host disease | ACC | 2 -0.968 | 0.036 | HLA-DMB, HLA-A, HLA-C, HLA-E, HLA-B, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-F, HLA-DQB1, HLA-DRB1, HLA-DQA1, HLA-DRB5, CD86, HLA-DOA |
| Graft versus host disease | ACC | 2 -0.968 | 0.036 | HLA-DMB, HLA-A, HLA-C, HLA-E, HLA-B, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-F, HLA-DQB1, HLA-DRB1, HLA-DQA1, HLA-DRB5, CD86, HLA-DOA |
| Jak stat signaling pathway | BRCA | 2 0.911 | 0.028 | IL6, IL12RB1, OSM, CNTFR, IL2RA, PIK3CG, STAT4, IL21R, LIF, SOCS1, JAK3, CSF2RB, IFNLR1, PIK3CD, IL7R, PIK3R5, IL15, IL2RB, IL24, IL6R, IL10RA, IL2RG, IL15RA, IL11RA, CTF1, PIM1, SPRY2, SOCS3 |
| Jak stat signaling pathway | LGG | 2 -0.918 | 0.01 | CSF3R, IL13RA1, SPRY4, IL15RA, SPRY1, IL10RA, SOCS2, STAT5A, IL6R, PIK3CD, SOCS3, LEPR, PIK3R5, OSMR, IL2RG, CTF1, GHR, CNTF, CISH, IL13RA2, SOCS1, JAK3, IL12RB1 |
| Maturity onset diabetes of the young | ESCA | 2 0.977 | 0.038 | FOXA3, FOXA2, MNX1, NR5A2, HNF1A, BHLHA15, HNF1B, PDX1, HNF4A, HNF4G |
| Maturity onset diabetes of the young | OV | 2 0.975 | 0.029 | FOXA2, BHLHA15 |
| Taste transduction | COAD | 2 0.972 | 0.016 | SCNN1B |
| Taste transduction | KIRP | 2 0.964 | 0.041 | PDE1A, ADCY4, PLCB2 |
| Axon guidance | TGCT | 1 0.897 | 0.018 | SEMA3C, NTN1, SLIT2, EFNA5, ROBO2, EPHB1, SRGAP3, EPHA3, PLXNB3, SEMA3E, SEMA5A, PAK6, ABLIM2, SEMA5B, RHOD, SEMA6D, SEMA3B, MET, EPHA2, UNC5B, SLIT3, EPHA4, SEMA3G, NTN4, EFNA2, EFNB2, EPHB2, PPP3CA, RND1, EPHA7, EFNB1, ROBO3, SEMA3A, EFNA1, SEMA3F, GNAI1, PLXNA3, CFL2, NFATC2, EPHB6, ROBO1, EPHB3, NRP1 |
| Calcium signaling pathway | BLCA | 1 0.92 | 0.048 | CD38, TNNC2, PLN, CACNA1C, GNA14, PDE1B, TRPC1, TBXA2R, GNAL, P2RX5, CALML5, ADORA2A, BD- KRB1, EDNRA, PDGFRA, EDNRB, PLCB2, ADCY7, ITPR1, ADCY4, SPHK1, NOS3, GRIN2D, ADCY9, MYLK, PDGFRB, BST1, PTAFR, ITPR2, F2R, CACNA1H |
| Citrate cycle tca cycle | PAAD | 1 0.976 | 0.027 | OGDHL, PCK1 |
ASGR1 in human cancers
| Dilated cardiomyopathy | BLCA | 1 0.944 | 0.045 | CACNA2D4, DMD, SGCA, ITGA9, PLN, TGFB2, CACNA1C, ACTC1, ITGA4, ITGA11, LAMA2, DES, ITGA7, TGFB3, ADCY7, ADCY4, ADCY9, ITGA1, SGCB |
| Ecm receptor interaction | LAML | 1 0.942 | 0.017 | THBS4, ITGA3, SDC3, GP9, COL5A1, TNC, SDC1, COL6A2, SDC4, ITGB3, ITGA7, ITGB5, COL6A1, THBS1, VWF |
| Hedgehog signaling pathway | KIRC | 1 0.959 | 0.036 | WNT5A, GAS1, GLI1, WNT2B, WNT5B, BMP8B, GLI3 |
| Hypertrophic cardiomyopathy hcm | LAML | 1 0.924 | 0.039 | SLC8A1, CACNA2D3, ITGA3, ACE, TNF, ITGB3, ITGA7, ITGB5, CACNB4, CACNB1, CACNA2D4, LMNA, ITGB7 |
| Leishmania infection | KIRP | 1 0.948 | 0.042 | HLA-DOB, FCGR3B, ITGA4, PRKCB, IL1B, HLA-DQA2, NCF4, TLR4, ITGAM, NCF1, FCGR2C, NCF2, TLR2, HLA-DOA, FCGR1A, MAPK11, FCGR3A, ITGB2, HLA-DQB1, HLA-DQA1, FCGR2A, HLA-DRB5, TGFB3, C3, HLA-DMA, HLA-DPB1, HLA-DRB1, HLA-DPA1, TGFB1, HLA-DMB, MAPK12, HLA-DRA |
| Leukocyte transendothelial migration | KIRP | 1 0.927 | 0.039 | RHOH, ITGA4, CLDN14, PRKCB, MYLPF, PIK3R5, MMP9, JAM2, VAV1, ITGAL, NCF4, ITGAM, NCF1, RASSF5, NCF2, CYBB, MMP2, MAPK11, CLDN5, RAC2, ITGB2, CXCR4, PECAM1, CDH5, JAM3, CXCL12, PIK3CD, PLCG2, ICAM1, THY1, SIPA1, MAPK12 |
| Linoleic acid metabolism | UVM | 1 0.968 | 0.049 | PLA2G4B, JMJD7-PLA2G4B |
| Mapk signaling pathway | LAML | 1 0.866 | 0.011 | CACNA2D3, HSPA6, MRAS, IL1R2, TNF, CD14, RRAS, HSPA1L, PRKCA, CACNB4, NR4A1, AKT3, CACNB1, MAP3K6, RRAS2, IL1R1, MAP2K6, RPS6KA2, MAPK13, NFKB2, MAPK7, CACNA2D4, RASGRP4, DUSP6, MAP3K14, FLNB, RELB, GADD45B, PRKACA, ARRB2, MAP2K1, MAP3K3, PAK1, CACNB3, JUN, CACNB2, DUSP1, IKBKG, DUSP7, PLA2G4B, DUSP5, DUSP2, CDC25B, HSPA1A, FOS, RPS6KA4, MAP3K12, MEF2C, TGFBR2, RASGRP1, HSPB1, MAPK3, FGFR1, PRKCB, FLNA, MAP3K11, HRAS, HSPA1B, MAP3K8, JMJD7-PLA2G4B, PLA2G4A, CRK, GRB2, TGFB1, DUSP3, RPS6KA1, NFKB1, MAP4K4, RAC1, FAS, GNA12, MAP2K3, CHP1, MKNK2, MAPKAPK2, MAP4K1, MAP4K2, TAB1, MAP3K2, MKNK1, IL1B |
| Nod like receptor signaling pathway | DLBC | 1 0.946 | 0.039 | CCL8, IL6, CXCL1, CCL13, CXCL8, CXCL2, CASP5, NLRP3, IL1B, NLRC4, NOD2, CARD6 |
| Pathways in cancer | TGCT | 1 0.791 | 0.016 | FGF17, WNT11, KITLG, HGF, MAPK10, WNT5A, BMP2, FGF8, FGF2, GLI3, RUNX1T1, MECOM, MMP1, ARNT2, TGFB2, HHIP, CXCL8, NKX3-1, BCL2, ITGA2, FGF7, WNT4, WNT6, FZD9, TGFA, FZD2, COL4A6, LAMB3, EGFR, FZD7, MET, CDKN2B, CCNA1, FZD1, PRKCA, FZD4, FGF19, CDK6, TCF7L2, CCND1, PDG- FRA, VEGFC, TCF7L1, BMP4, JUN, SOS2, FGF12, LAMC2, WNT5B, ITGA3, MITF, PGF, VEGFA, FN1, FAS, LAMA2, CDKN1A, FZD6, CBLC, PLD1, TGFB3, PDGFRB, LAMB1, CDKN2A, PDGFA, PIK3R1, CDH1, LAMC3, EPAS1, FGF18, ERBB2, RXRA, GLI2, FOS, EGLN3, ITGAV, LEF1, LAMB2, TGFBR2 |
| Phenylalanine metabolism | UVM | 1 0.986 | 0.025 | AOC3, IL4I1 |
| Primary immunodeficiency | KICH | 1 0.966 | 0.032 | CD3E, BTK, ADA, DCLRE1C, CD3D, RFXAP, LCK, CD8A, JAK3, IL2RG, PTPRC, CIITA, CD4 |
| Proximal tubule bicarbonate reclamation | BRCA | 1 -0.971 | 0.033 | GLUD1, GLS2, GLUD2, ATP1A4 |
| Regulation of actin cytoskeleton | TGCT | 1 0.826 | 0.044 | FGF17, MYL7, FGF8, FGF2, ITGA11, F2, ITGB6, SCIN, ITGB8, TMSB4Y, ITGA2, ITGA8, CHRM3, FGF7, PAK6, PDGFD, EGFR, PDGFC, MYLPF, FGF19, PDGFRA, VAV3, SSH3, ITGA1, ACTN2, SOS2, FGF12, ITGA3, FN1, CFL2, PDGFRB, PDGFA, TMSB4XP8, PIK3R1, ARHGEF4, MYL5, ARHGEF6, ITGA5, MYH14, FGF18, ITGA7, MYLK, ITGAV |
| Starch and sucrose metabolism | KICH | 1 0.956 | 0.03 | PYGL, PGM2L1, MGAM, HK2, UGT1A6 |
| Steroid hormone biosynthesis | KIRC | 1 0.961 | 0.035 | CYP21A2, HSD11B1, UGT1A3 |
| T cell receptor signaling pathway | CHOL | 1 -0.931 | 0.038 | LCP2, AKT3, CD3D, VAV1, CARD11, CD3E, PIK3CD, PTPRC, ZAP70, CD247, LCK, CD8A, PIK3R5, TEC, CTLA4, GRAP2, PRKCQ, ITK, CD3G, RASGRP1, PDCD1, CD8B |
| Toll like receptor signaling pathway | LAML | 1 0.908 | 0.05 | TLR5, TLR7, TICAM2, TLR8, TNF, CD14, CD40, CD86, LY96, CCL3, AKT3, TLR6, TLR4, MAP2K6, TLR1, MAPK13, TLR9, CTSK, PIK3R5, TICAM1, IRF7, TLR2, TOLLIP, MAP2K1, JUN, CXCL8, MYD88, IKBKG, CCL4, IRF5 |
| Type i diabetes mellitus | UCS | 1 -0.967 | 0.04 | CD86, FAS, GZMB, IL12A, IL1B, GAD1, HLA-DQA2 |
| Wnt signaling pathway | LAML | 1 0.883 | 0.041 | FZD2, APC2, WNT5B, FZD1, CAMK2D, PPARD, PRKCA, FZD5, TCF7L2, FRAT1, TBL1X, FZD6, PORCN, PRKACA, PPP2R5B, JUN, SMAD3, PLCB3 |
ASGR1 in human cancers
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2
4
3
ulixertinib IC50
2
0
FT-1518 IC50
2
AZD-0156 IC50
LGH-447 IC50
1
3
ABBV-744 IC50
2
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Cobimetinib (isomer 1) IC50
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3
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ENMD-2076 Precursor IC50
2
CEP-28122 IC50
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CEP-37440 IC50
2
CC-115 IC50
2
Vemurafenib IC50
2
AZD-3463 IC50
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Milciclib IC50
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High
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Palbociclib IC50
CCT-128930 IC50
Encorafenib IC50
LY-2835219 IC50
2
SB-202190 IC50
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PF-03084014 diastereomer 1 IC50
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3
2
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BMS-536924 IC50
3
GSK-2606414 IC50
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